The DOI matches its Crossref record; 238 of the 238 references it cites were found in a registry or an index; 10 of the 304 sources its claims cite were checked for support.
Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey
Open accessAbstract
Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provides partial treatment, often constrained by two competing narratives, optimistically focused on replacing biased recruiter decisions or pessimistically pointing to the automation of discrimination. Whether, and more importantly what types of , algorithmic hiring can be less biased and more beneficial to society than low-tech alternatives currently remains unanswered, to the detriment of trustworthiness. This multidisciplinary survey caters to practitioners and researchers with a balanced and integrated coverage of systems, biases, measures, mitigation strategies, datasets, and legal aspects of algorithmic hiring and fairness. Our work supports a contextualized understanding and governance of this technology by highlighting current opportunities and limitations, providing recommendations for future work to ensure shared benefits for all stakeholders.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This multidisciplinary survey synthesizes computer science, legal, and social-science literature on fairness and bias in algorithmic hiring, covering pipeline stages, bias-conducive factors, fairness measures, mitigation strategies, datasets, and EU/US law. It argues that algorithmic hiring can either mitigate or amplify structural inequalities, and that contextualized, multidisciplinary evaluation is needed to ensure shared benefits.
Key findings
- The survey maps four hiring stages—sourcing, screening, selection, and evaluation—and identifies bias-conducive factors across institutional biases, individual preferences, and technology blindspots, including direct discrimination, horizontal/vertical segregation, cultural fit, ad delivery optimization, accessibility issues, and biased psychological or background-check tools.
- It reports market scale: over 250 AI tools for HR, an average job posting yielding more than 100 candidates, and a 2023 survey in which 98% of 300 US HR leaders said software and algorithms would assist them with layoff decisions.
- It unifies fairness measures (outcome, accuracy, impact, process, representational) and mitigation strategies (pre-, in-, post-processing), noting that most measures focus on outcome fairness and that proxy reduction is popular but yields limited improvements when inequity is less significant.
- Dataset review: 20 datasets, mostly early pipeline stages—11 sourcing, 8 screening, 0 selection, 1 evaluation; gender is the most common sensitive attribute, race/ethnicity appears in 6 of 20, and disability, religion, and sexual orientation are largely missing; English and US/Netherlands contexts dominate.
Methods
- Multidisciplinary survey carried out by a team with mixed computer science, law, and philosophy backgrounds, with input from practitioners; Sections 4–6 result from a systematic literature review, while other sections draw on technical reports, industry white papers, and legal literature.
- Systematic review used IEEE Xplore, ACM Digital Library, and Google Scholar; query `algorithmic ∧ hiring ∧ fairness` with expanded terms; top 100 results per engine; manual inclusion of articles treating fairness in algorithmic hiring quantitatively; forward/backward snowballing and related surveys.
- Analysis provides unified notation and dimensions for fairness measures, a taxonomy of mitigation methods, and a compilation of datasets in Table 4.
Limitations
- Paper-stated: fairness measures often focus narrowly on local algorithmic outcomes and miss the broader socio-technical context; outcome fairness at every stage does not guarantee fair outcomes across the whole pipeline; data have reduced geographical and linguistic coverage and missing sensitive attributes; legal and privacy constraints limit sensitive-data use; most fairness approaches are restricted to proxy reduction.
- Paper-stated: video-based systems yield the largest disparities and are unreliable under varying conditions; target variables such as employability, motivation, and job performance have questionable validity; there are no datasets for selection and only one for evaluation.
- Evident from the text: as a survey, it does not present new empirical experiments; the systematic review is limited to top 100 results per engine and specific inclusion criteria, which may miss relevant work; dataset coverage is skewed toward English, US, and Netherlands contexts.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
10 of 304 cited sources checked · 5 supported · 0 flagged · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“Self-promotion gaps related to gender have been documented in the hiring domain [5] and beyond; men tend to self-evaluate higher than women even without intrinsic incentives [87].”
What the sources say
- Supportedcites [5] [5] Kristen M. Altenburger, Rajlakshmi De, Kaylyn Frazier, Nikolai Avteniev, and Jim Hamilton. 2017. Are There Gender Differences in Professional Self-Promotion? An Empirical Case Study of LinkedIn Profiles Among Recent MBA Graduates. In Proc. of the Eleventh International Conf. on Web and Social Media, ICWSM 2017, Montréal, Québec, Canada, May 15-18, 2017 . AAAI Press, 460-463. https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15615 - source
The Abstract reports gender differences in self-promotion on LinkedIn among matched recent MBA graduates and places the study in the professional hiring pipeline. The Conclusion reports that female users had lower odds than male users of including Summary and Job Description fields. The citation supports the hiring-domain portion of the sentence; the separate claim about self-evaluation is attributed to [87]. (LLM, conf=0.91)
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“Multiple studies have shown that maximization of cost-effectiveness based on ad delivery metrics, such as impressions or clicks, makes delivery skewed in accordance with gender and race stereotypes in jobs [4, 10, 61, 158].”
What the sources say
- Supportedcites [4] [4] Muhammad Ali, Piotr Sapiezynski, Miranda Bogen, Aleksandra Korolova, Alan Mislove, and Aaron Rieke. 2019. Discrimination through Optimization: How Facebook's Ad Delivery Can Lead to Biased Outcomes. Proc. ACM Hum. Comput. Interact. 3, CSCW (2019), 199:1-199:30. https://doi.org/10.1145/3359301 - source
The Employment ads section reports substantial gender and racial skew across job ads with identical targeting and bidding, and attributes the differences in part to ad-delivery optimization. Section 3.4 specifies link clicks as the optimization goal in the experiments; the Objective and bidding section also describes impressions and clicks as Facebook optimization objectives. The reported job-ad skew is evidence for the claim, although these experiments used click optimization rather than testing impression optimization for job ads. (LLM, conf=0.88)
- Not checked yetcites [10] [10] Julia Angwin, Noam Scheiber, and Ariana Tobin. 2017. Dozens of Companies Are Using Facebook to Exclude Older Workers From Job Ads . Machine Bias. ProPublica, New York, NY, USA. https://www.propublica.org/article/facebookads-age-discrimination-targeting -
- Not matchedcites [61]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [158]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:boosted· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Measures to quantify stereotypes and representational harms are relatively understudied [1, 92], despite being a fundamental driver in the reinforcement of societal biases.”
What the sources say
- Supportedcites [1] [1] Mohsen Abbasi, Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. 2019. Fairness in representation: quantifying stereotyping as a representational harm. In Proc. of the 2019 SIAM International Conf. on Data Mining, SDM 2019, Calgary, Alberta, Canada, May 2-4, 2019 , Tanya Y. Berger-Wolf and Nitesh V. Chawla (Eds.). SIAM, 801-809. https://doi.org/10.1137/1.9781611975673.90 - source
The Introduction says fairness research has typically studied harms of allocation rather than representation, then identifies a need to quantify representational harm and design measures sensitive to stereotyping. Section 3 presents stereotyping measures. Together, these passages support the claim that such measures are relatively understudied. Page numbers are unavailable in our copy. (LLM, conf=0.85)
- Not matchedcites [92]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Ad text and images can further skew the audience [4].”
What the sources say
- Partly supportedcites [4] [4] Muhammad Ali, Piotr Sapiezynski, Miranda Bogen, Aleksandra Korolova, Alan Mislove, and Aaron Rieke. 2019. Discrimination through Optimization: How Facebook's Ad Delivery Can Lead to Biased Outcomes. Proc. ACM Hum. Comput. Interact. 3, CSCW (2019), 199:1-199:30. https://doi.org/10.1145/3359301 - sourceExperimental — accuracy not yet measured
The ‘Individual components’ impact on ad delivery’ section (p. 15) finds that adding an image dramatically changed the gender composition of the audience. It also reports that adding text without an image did not appreciably change delivery, so the claim is not fully supported for text. (LLM, conf=0.86)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Direct discrimination, in turn, is generally much more difficult to legally justify [2].”
What the sources say
- Supportedcites [2] [2] Jeremias Adams-Prassl, Reuben Binns, and Aislinn Kelly-Lyth. 2023. Directly discriminatory algorithms. The Modern Law Review 86, 1 (2023), 144-175. - source
In “The broad(er) scope of direct discrimination” (page unavailable), the paper describes indirect discrimination as having a “larger pool of justifications.” “A normative concern” explains that direct-discrimination law excludes certain protected characteristics from permissible reasons for less favourable treatment. Together, these passages support the general comparison in the claim. (LLM, conf=0.86)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The simple fact of using algorithmic decision-making can reduce avoidance by vulnerable candidates [13].”
What the sources say
- Partly supportedcites [13] [13] Mallory Avery, Andreas Leibbrandt, and Joseph Vecci. 2023. Does Artificial Intelligence Help or Hurt Gender Diversity? Evidence from Two Field Experiments on Recruitment in Tech. http://monash-econ-wps.s3.amazonaws. com/RePEc/mos/moswps/2023-09.pdf - sourceExperimental — accuracy not yet measured
In “The Application Decision” (page unavailable), the paper reports that announcing AI rather than human evaluation increased women’s completion of a tech job application. Section 4.4 links this to lower anticipated bias. This supports reduced application avoidance for women in the studied setting, but not the broader claim about vulnerable candidates generally or algorithmic decision-making in other settings. (LLM, conf=0.82)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Partly supported
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“These measures are rare in the literature, as they require a broader understanding and modeling of the sociotechnical system around the decision-making algorithm.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“Outcome fairness [114] is the most common.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“It should be noted that we focus on group fairness; we found a single article treating individual fairness [172].”
What the sources say
- Not matchedcites [172]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“Most of the fairness measures considered in hiring focus on outcome equity; the field is strongly influenced by the 80% rule (Section 8.2), which is appreciated as a quantitative rule of thumb, but also often criticized [74, 267].”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Nevertheless, several fairness measures adopted in hiring are difficult to read in absolute terms, making it challenging to understand the severity of fairness violations based on their values.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For this reason, the separation between classification and ranking is less rigid than in other domains, and the question of which measures are best suited to evaluate these systems arises.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“English is by far the dominant language”
Strength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Rests on an assumption: The datasets collected in Table 4 are representative of the research on fairness in algorithmic hiring.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The vast majority of datasets describe the early stages of the hiring pipeline, i.e. sourcing (11 out of 20) and screening (8 out of 20)”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: The datasets collected in Table 4 are representative of the research on fairness in algorithmic hiring.
None of the sources cited for this claim have been checked yet.
In the paper
“attributes such as disability, religion, and sexual orientation are simply missing”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: The datasets collected in Table 4 are representative of the research on fairness in algorithmic hiring.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“For example, agency (orientation towards leadership and goal attainment) is stereotypically associated with men, while communion (warmth and propensity for care) is frequently associated with women [72, 118], with far-reaching effects of gender roles and expectations in employment [118, 215].”
What the sources say
- Not matchedcites [118]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [215]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes? Checkable: unclear? Load-bearing: unclear✓ Based: passes- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hiring data and algorithms can display undesirable groupwise patterns caused by Bias Conducive Factors (BCFs) affecting the employment domain.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearNone of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Strong gender patterns in diverse regions of the world are linked to persistent gender stereotypes about agency and communion that shape our perception and expectations [42, 83, 109].”
What the sources say
- Not matchedcites [42]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [83]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [109]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:boosted· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“More specifically, voice timbre and physical appearance in videos may be used as proxies for gender, while names and spoken languages in CVs may correlate heavily with certain migration backgrounds.”
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“More specifically, voice timbre and physical appearance in videos may be used as proxies for gender, while names and spoken languages in CVs may correlate heavily with certain migration backgrounds.”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesNone of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Famous correspondence studies have shown that black applicants are less likely to be contacted after applying for a job compared to otherwise identical white candidates [23, 147].”
What the sources say
- Not matchedcites [23]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [147]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:boosted· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“Stereotypes are sustained by culture, socialization, and experience [50, 50].”
What the sources say
- Not matchedcites [50]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Numerous field experiments using matched pairs of applicants have highlighted discrimination against women and non-whites; experiments also point to a risk of direct discrimination against disabled and older applicants [208, 215].”
What the sources say
- Not matchedcites [208]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [215]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“They are often acquired at an early age [134] and can be activated unconsciously [181].”
What the sources say
- Not matchedcites [134]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Wage gaps are a key part of power differences between genders and races [40, 81, 121, 269].”
What the sources say
- Not matchedcites [40]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [81]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [121]
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- Not matchedcites [269]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Most field experiments, for example, demonstrate discrimination against women in men-dominated jobs and vice versa [215, 216].”
What the sources say
- Not matchedcites [215]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [216]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:boosted· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is especially true for algorithms trained to make inferences from inputs that are strongly correlated with sensitive attributes.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Numerous field experiments using matched pairs of applicants have highlighted discrimination against women and non-whites; experiments also point to a risk of direct discrimination against disabled and older applicants [208, 215].”
What the sources say
- Not matchedcites [208]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [215]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“Evaluations of cultural fit by recruiters, subjective or objective, can contribute to maintaining a uniform workforce in the company, especially in more senior positions, reinforcing horizontal and vertical segregation [3, 219].”
What the sources say
- Not checked yetcites [3] [3] Ifeoma Ajunwa. 2019. The paradox of automation as anti-bias intervention. Cardozo L. Rev. 41 (2019), 1671. -
- Not matchedcites [219]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesNone of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Furthermore, a combination of vertical job segregation and homophily entails that minority employees who are not well represented in management positions may receive lower ratings when manager-employee personal relationships have a positive influence on evaluations and promotions [26, 31, 152]”
What the sources say
- Not matchedcites [26]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [31]
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- Not matchedcites [152]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“In turn, this ends up shaping expectations about the qualities, priorities, and needs that people have about themselves and others, including, and perhaps especially, about work [29, 50, 118, 248].”
What the sources say
- Not matchedcites [29]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [50]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [118]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [248]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“Stereotype activation and entrenchment is not always direct; stereotypes about a group can be activated by proxies [211].”
What the sources say
- Not matchedcites [211]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Women are frequently penalized for gender norm violations that make them appear more agentic (e.g. competent and self-confident) and less communal (e.g. kind and warm) than stereotypically expected [115, 195, 247].”
What the sources say
- Not matchedcites [115]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [195]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [247]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“Workplaces and labor markets are fraught with biases, imbalances, and patterns of discrimination against vulnerable groups, including women, ethnic minorities, and people with disabilities [15, 23, 215].”
What the sources say
- Not checked — no full text of the sourcecites [15] [15] Ghazala Azmat and Barbara Petrongolo. 2014. Gender and the labor market: What have we learned from field and lab experiments? Labour economics 30 (2014), 32-40. - source
- Not matchedcites [23]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [215]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“They are often acquired at an early age [134] and can be activated unconsciously [181].”
What the sources say
- Not matchedcites [181]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As a result, prospective job applicants and their chances of success are increasingly influenced by algorithmic hiring technology, including automated job descriptions, resume parsers, and video interviews.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“Even when outspokenly rejected, stereotypes affect the lives of individuals both descriptively and prescriptively based on coarse categories [76].”
What the sources say
- Not matchedcites [76]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Stereotype violation has a disparate effect on the 'transgressor', depending on their gender.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesNone of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Similar results have been found in interview evaluations provided by US-based judges, showing a preference for standard American accents over international ones [67, 164, 205].”
What the sources say
- Not matchedcites [67]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [164]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [205]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Disparate performance of language processing and computer vision tools has been widely demonstrated with respect to gender, race, and other sensitive attributes [27, 37, 241].”
What the sources say
- Not matchedcites [27]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [37]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [241]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Not matched
In the paper
“This effect may be exacerbated by homophily and biased preferential attachment [14].”
What the sources say
- Not checked — no full text of the sourcecites [14] [14] Chen Avin, Barbara Keller, Zvi Lotker, Claire Mathieu, David Peleg, and Yvonne-Anne Pignolet. 2015. Homophily and the Glass Ceiling Effect in Social Networks. In Proc. of the 2015 Conf. on Innovations in Theoretical Computer Science, ITCS 2015, Rehovot, Israel, January 11-13, 2015 , Tim Roughgarden (Ed.). ACM, 41-50. https://doi.org/10.1145/2688073. 2688097 - source
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Willingness to commute is another individual factor with gender-related patterns.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesNone of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Systematic differences in the results of psychometric tests between subgroups [52, 130], caused by low discriminant validity [133] or construct contamination [8], can be especially problematic for tests embedded in larger resource allocation processes, such as hiring systems.”
What the sources say
- Not matchedcites [52]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [130]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [133]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [8] [8] American Educational Research Association, American Psychological Association, and National Council on Measurement in Education. 2014. The standards for educational and psychological testing. (2014). -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By removing the discriminatory effect of any one factor, we can hope to reduce its influence on the other factors that reinforce each other in a discriminatory way.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Measures to quantify stereotypes and representational harms are relatively understudied [1, 92], despite being a fundamental driver in the reinforcement of societal biases.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes? Checkable: unclear? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Since the factors that create bias are interrelated and mutually reinforcing, by halting or ameliorating one BCF, we may introduce positive feedback loops on other BCFs.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“Job satisfaction influences job commitment [20, 228].”
What the sources say
- Not checked — no full text of the sourcecites [20] [20] Arthur G Bedeian, Gerald R Ferris, and K Michele Kacmar. 1992. Age, tenure, and job satisfaction: A tale of two perspectives. Journal of Vocational behavior 40, 1 (1992), 33-48. - source
- Not matchedcites [228]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“Self-promotion gaps related to gender have been documented in the hiring domain [5] and beyond; men tend to self-evaluate higher than women even without intrinsic incentives [87].”
What the sources say
- Not matchedcites [87]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Differences in platform engagement broadly divide people into frequent and infrequent users.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“Interaction biases between employers and hiring technology can amplify tiny differences in algorithmic output [16].”
What the sources say
- Not checked — no full text of the sourcecites [16] [16] Ricardo Baeza-Yates. 2018. Bias on the web. Commun. ACM 61, 6 (2018), 54-61. https://doi.org/10.1145/3209581 - source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesNone of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Women tend to be more restrictive in their choice of job search area [78, 161].”
What the sources say
- Not matchedcites [78]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [161]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Since the factors that create bias are interrelated and mutually reinforcing, by halting or ameliorating one BCF, we may introduce positive feedback loops on other BCFs.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Among other critical aspects, criminal background checks before employment run the risk of feeding the racial disparity of policing [203] and other areas of the criminal justice system [151] into hiring systems.”
What the sources say
- Not matchedcites [203]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [151]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“More general examples include position bias that causes underexposure risks for candidates that are not at the top of a ranking [60] and automation bias leading to over-reliance on technology and reduced human oversight [106, 156].”
What the sources say
- Not matchedcites [60]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“Although ostensibly a personal outcome of female candidates, unsuccessful salary negotiation is also based on an unfair status quo that influences group expectations [110].”
What the sources say
- Not matchedcites [110]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Gender asymmetries in caregiving responsibilities put women at a systematic disadvantage [93, 162].”
What the sources say
- Not matchedcites [93]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [162]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“Wording itself can also sustain inequality: gendered wording in job advertisements that makes use of stereotypically agentic (male) language, such as 'leadership' or 'delivery', can make a position less attractive for women [102].”
What the sources say
- Not matchedcites [102]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“In the context of algorithmic hiring, it is especially relevant for the wording of job descriptions which can skew the probability of application for different demographics [102].”
What the sources say
- Not matchedcites [102]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“Biased psychological assessment performance for minorities can result in systematic disadvantages [212].”
What the sources say
- Not matchedcites [212]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Historically disadvantaged groups such as transgender, nonbinary, female, black, and disabled workers are more likely to experience discrimination and harassment on the job [225, 228, 261].”
What the sources say
- Not matchedcites [225]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [228]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [261]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Not matched
In the paper
“The connection recommender system in a professional social network was found to underperform for women, recommending them less frequently for professional connections than men with similar success rates [39].”
What the sources say
- Not matchedcites [39]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“The same alienating effect can occur by signaling an unwelcoming workplace culture at other stages of the hiring pipeline [274].”
What the sources say
- Not matchedcites [274]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Culture-based avoidance or attraction, is a self-selection BCF that influences mainly the sourcing stage.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Ad delivery optimization can skew the audiences reached by job advertisements.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“During the sourcing stage, for instance, recruiters tend to focus on very specific credentials, certifications, and keywords that exclude atypical individuals from the initial pool of candidates, despite having the right skills [101].”
What the sources say
- Not matchedcites [101]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“Post-processing approaches, such as DetGreedy, are the easiest to integrate into existing systems, as they can be modularly added to algorithmic pipelines [257].”
What the sources say
- Not matchedcites [257]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“Parasurama and Sedoc [194] show that SHAP-based scraping can achieve a sizeable sAUC drop with a limited negative impact on performance.”
What the sources say
- Not matchedcites [194]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“in conjunction with its controversy in US employment law [22, 165]”
What the sources say
- Not matchedcites [22]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [165]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Not matched
In the paper
“The definition of sensitive attributes in antidiscrimination law is informed by the historical (dis)advantage of specific groups [230].”
What the sources say
- Not matchedcites [230]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Not matched
In the paper
“This method is tested on a friendship recommendation engine in a live proprietary system (most likely LinkedIn ),”
What the sources say
- Not matchedcites [185]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedSingle caseTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“It contains 135 resumes of candidates of Chinese, Malaysian, and Indian origin (the predominant ethnic groups in Singapore) who applied for vacancies in Singapore's accounting and finance sectors.”
What the sources say
- Not matchedcites [69]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Across multiple studies, systems trained on video signals are shown to yield the largest disparities and disadvantages for protected groups [33, 120, 276].”
What the sources say
- Not matchedcites [33]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [120]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [276]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Not matched
In the paper
“outcome fairness (DI) also improves, but only for systems that were initially very unfair.”
What the sources say
- Not matchedcites [33]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“It is tested on multimodal data and found to decrease gender predictability (sAUC) for paraverbal and visual data,”
What the sources say
- Not matchedcites [33]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By prioritizing items with higher scores in G 𝐿 , DetGreedy may end up violating some minimum representation constraints at some rank, i.e. ∃ 𝑔 s.t. 𝑁 𝑘 𝑔 < ⌊ 𝐷 𝑔 · 𝑘 ⌋ . This happens when more than one group is in G 𝐻 .”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Post-processing methods, such as DetGreedy and CDF restoring, typically require knowledge of sensitive attributes during runtime.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“and only marginally improving DI.”
What the sources say
- Not matchedcites [33]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Not matched
In the paper
“DetGreedy is implemented in LinkedIn Recruiter to ensure equitable gender representation in candidate search ;”
What the sources say
- Not matchedcites [103]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“Indeed, most post-processing methods in algorithmic hiring are proposed and deployed at LinkedIn ,”
What the sources say
- Not checked — no full text of the sourcecites [12] [12] Aditya Auradkar, Chavdar Botev, Shirshanka Das, Dave De Maagd, Alex Feinberg, Phanindra Ganti, Lei Gao, Bhaskar Ghosh, Kishore Gopalakrishna, Brendan Harris, Joel Koshy, Kevin Krawez, Jay Kreps, Shi Lu, Sunil Nagaraj, Neha Narkhede, Sasha Pachev, Igor Perisic, Lin Qiao, Tom Quiggle, Jun Rao, Bob Schulman, Abraham Sebastian, Oliver Seeliger, Adam Silberstein, Boris Shkolnik, Chinmay Soman, Roshan Sumbaly, Kapil Surlaker, Sajid Topiwala, Cuong Tran, Balaji Varadarajan, Jemiah Westerman, Zach White, David Zhang, and Jason Zhang. 2012. Data Infrastructure at LinkedIn. In IEEE 28th Internation source
- Not matchedcites [104]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“a separate application to each intersectional group (e.g. black women) is the most straightforward extension, but increasingly smaller groups run the risk of unstable normalization.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Measures of outcome and impact fairness, such as DD and SD, tend to be more interpretable, since they are typically defined as differences between the probability of obtaining desirable outcomes for different groups.”
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“This approach reduces the ability of adversaries to infer an individual's gender and ethnicity from their hidden representations as measured by sAUC in a job classification task.”
What the sources say
- Not matchedcites [119]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Not matched
In the paper
“De-Arteaga et al. [63] study the effectiveness of this approach in job classification from short biographies. They find it to be moderately effective in reducing TPRD.”
What the sources say
- Not matchedcites [63]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“Rus et al. [222] show the suitability of this method to improve selected process, outcome, and impact fairness indicators in a job recommendation scenario,”
What the sources say
- Not matchedcites [222]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Due to redundant encoding of sensitive information in the data, rule-based approaches often represent a weak baseline with limited impact on fairness.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Additionally, postprocessing requires run-time access to the sensitive attributes of all data subjects,”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“This approach is also adopted by vendors, such as Pymetrics and Hirevue , upon detecting violations of the 80% rule in pre-deployment audits [124, 209].”
What the sources say
- Not matchedcites [124]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [209]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“They confirm the suitability of this method to improve process fairness (sAUC);”
What the sources say
- Not matchedcites [33]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Furthermore, it is worth noting regulation proposals against inference of emotions, states of mind, or intentions from face images in the workplace [84, 85].”
What the sources say
- Not matchedcites [84]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [85]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“In line with recent literature, we find no graph dataset on algorithmic hiring [46, 71].”
What the sources say
- Not matchedcites [46]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [71]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A very clear trend in the literature is the popularity of proxy reduction methods, such as adversarial inference, that target process fairness (sAUC) in pursuit of outcome fairness (e.g. DI, TPRD).”
Strength:boosted· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesNone of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“it proves ineffective in reducing the amount of gender information contained in resumes, as measured by sAUC, in alignment with prior art [108].”
What the sources say
- Not matchedcites [194]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [108]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“while increasing predictability for verbal features”
What the sources say
- Not matchedcites [33]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Not matched
In the paper
“and may be suited for settings where sensitive attributes are unavailable during training.”
What the sources say
- Not matchedcites [120]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- The authors state this as an assumption: Face data contain enough information on sensitive features such as gender and race that successful debiasing can be achieved without explicit sensitive attribute information.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“Overall, balanced sampling can reduce the influence of job segregation on hiring algorithms.”
What the sources say
- Not checked yetcites [11] [11] Adam Mehdi Arafan, David Graus, Fernando P Santos, and Emma Beauxis-Aussalet. 2022. End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates. In 2nd Workshop on Recommender Systems for Human Resources, RecSys-in-HR 2022 . CEUR-WS, 1-8. -
- Not matchedcites [276]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“This is especially true in hiring, where surveys and metaanalyses find consistent biases against women and ethnic minorities [23, 147, 208].”
What the sources say
- Not matchedcites [23]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [147]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [208]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The organization is also responsible if it uses an algorithmic system provided by, for instance, a company”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Rests on an assumption: The organization using an algorithmic system is responsible if the system discriminates illegally.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“general non-discrimination law applies to new forms of algorithmic discrimination, also if that discrimination happens indirectly or accidentally”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“This GDPR rule makes it difficult to use sensitive data to audit or train algorithmic systems [254].”
What the sources say
- Not matchedcites [254]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“EASYRECRUE present an adversarial method that removes sensitive information from latent representations of neural networks that were trained for predicting 'hireability' given (features that represent) facial expressions of candidates in job interviews [120]”
What the sources say
- Not matchedcites [120]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“Rather, the disparate impact doctrine prohibits actions that are seemingly neutral, but significantly disadvantage members of a protected group.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“They are frequently annotated, rather than self-reported, in textual and visual datasets, due to these data modalities carrying strong proxies for sensitive attributes”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear? Based: unclearIn the paper
“In the case of LinkedIn 's Talent Search [103], the DetGreedy algorithm was implemented to have the ranker's top𝑘 output reflect the gender distribution of job seekers that meet the requirements of a recruiter-issued query”
What the sources say
- Not matchedcites [103]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“FairCVs [199] is a synthetic CV dataset combining short bios from Bias in bios [63], face images taken from the DiveFace database [180], and numerical features emulating desirable aspects such as availability or previous experience.”
What the sources say
- Not matchedcites [199]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [63]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [180]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“Geyik et al. [103]'s re-ranking method for bias mitigation can be considered computationally cheap as only a subset of a model's output needs to be processed (i.e., top𝑘 items)”
What the sources say
- Not matchedcites [103]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Bias mitigation through pre-processing, such as counterfactual data augmentation, and in-processing, such as feature weighting, can be comparatively more resource intensive as they involve (re-)training models”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“Peng et al. [200] show that 'overcompensation' (i.e., artificially over-representing a gender in the output of a ranking system) as an intervention strategy for a hiring algorithm can in some cases mitigate human bias further down the hiring funnel”
What the sources say
- Not matchedcites [200]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“These methods, also offered by vendors such as Textmetrics and Textio , can be used for mitigating specific types of biases, e.g., age bias in job descriptions [98], or gender bias in biographies [63]”
What the sources say
- Not matchedcites [98]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [63]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“given the growing push to adopt this technology in later stages [259], there is a tangible risk that algorithms for selection and evaluation will be quietly deployed without a clear understanding of risks and limitations”
What the sources say
- Not matchedcites [259]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“the rapid uptake of LLMs has pulled into focus biases that arise from LLM-based natural language generation”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“availability of and access to sensitive attributes may require job seekers' explicit consent, which can be difficult to acquire at the scale needed for some bias mitigation methods”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“it points to a lack of established best practices around target variables”
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“This is expected given the industry's tendency to use algorithms primarily for sourcing and screening [125, 166]”
What the sources say
- Not matchedcites [125]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [166]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Different bias mitigation methods have different requirements around the availability of sensitive data, which can further steer mitigation method selection”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“They find that the types of jobs recommended follow common gender and nationality-based stereotypes”
What the sources say
- Not matchedcites [224]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A drawback of these dictionary-based methods is a different side of the same coin: due to reliance on domain, context, and task-specific data (dictionaries) they do not transfer well over different problems (e.g., scraping gendered words from resumes vs. substituting words associated with age discrimination in job descriptions require wholly different sets of terms), languages, or geographies”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“while these additional costs may be incurred at training time, the resulting models do not incur additional costs at inference time”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“even if an organization can prove that it did not know that its algorithmic system discriminated unfairly, that will not help the organization”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Generally speaking, consent is not freely given, and thus not valid, if an employer asks a job applicant or employee for their consent, because of the unequal power relation.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the absence of specific legal rules on non-discrimination in algorithmic hiring, general non-discrimination law applies”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear? Based: unclearA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“post-processing methods that require sensitive attributes for all candidates at inference time can be unrealistic”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A simple yet effective mitigation strategy here is prompt engineering: by appending the phrase 'in an inclusive way' to the prompt, previously gendered pronouns are replaced with third person pronouns ('they/their')”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear? Based: unclearNone of the sources cited for this claim have been checked yet.
In the paper
“different experiments by practitioners show that outcome fairness does not need to come at the cost of utility”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“Arafan et al. [11] find that their pre-processing mitigation method of rebalancing training data may even improve utility over non-bias-mitigated methods, in an offline experiment using real data from an international HR company”
What the sources say
- Not checked yetcites [11] [11] Adam Mehdi Arafan, David Graus, Fernando P Santos, and Emma Beauxis-Aussalet. 2022. End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates. In 2nd Workshop on Recommender Systems for Human Resources, RecSys-in-HR 2022 . CEUR-WS, 1-8. -
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“Among other things, GDPR bans the use of special categories of personal data (sometimes called sensitive data). These are data on, for example, someone's ethnicity, religion, trade union membership, health, or sexual orientation (article 9 GDPR).”
What the sources say
- Not matchedcites article 9 GDPR
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
In the paper
“Salinas et al. [224] show how LLMs exhibit gender and nationality bias when generating job recommendations for job seekers of different genders and nationalities”
What the sources say
- Not matchedcites [224]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This lack of access to sensitive attributes means some bias mitigation methods may be less suited than others”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“we see practitioners experiment with alternative pre-processing methods in algorithmic hiring, such as synthetic tabular data generation [197] for measuring [256] and mitigating bias [11]”
What the sources say
- Not matchedcites [197]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [256]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [11] [11] Adam Mehdi Arafan, David Graus, Fernando P Santos, and Emma Beauxis-Aussalet. 2022. End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates. In 2nd Workshop on Recommender Systems for Human Resources, RecSys-in-HR 2022 . CEUR-WS, 1-8. -
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“while low, the additional computational costs will be incurred for each ranker output”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Common bias mitigation methods that cater exclusively to textual data include rule-based scraping or dictionary-based methods”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Aspects such as task, model complexity, architecture, training parameters, dataset size, and composition influence the scalability and efficiency of bias mitigation strategies”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Fairness evaluations by practitioners on pre-deployment test sets, such as Pymetrics Bias Group (Section 6.3), may quickly become obsolete.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A model deemed accurate and fair in the old context may perform poorly in the new one.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Algorithms for hiring can consider large pools of candidates, avoiding the preliminary exclusion of unusual profiles, as often done by human recruiters under time constraints.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesNone of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Therefore, many contributions to the literature on technical algorithmic fairness have provided tools to ensure that this rule is not violated at the statistical level [94, 278, 279].”
What the sources say
- Not matchedcites [94]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [278]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [279]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Data-driven algorithms tend to encode individual and societal biases.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“However, courts will generally, both in the EU and in the US, look at factors beyond mere scores and numbers to determine whether a legally relevant disadvantage exists [112, 260].”
What the sources say
- Not matchedcites [112]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [260]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Practitioners have to comply with these local rules if their model is applied in these jurisdictions.”
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In conjunction with current job segregation, this means that the most important features are inevitably skewed against historically disadvantaged groups.”
Strength:boosted· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Rests on an assumption: Previous experience is a preeminent feature for assessing candidates.
- Rests on an assumption: Current job segregation exists.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hence, several states and municipalities have taken the initiative and enacted AI hiring laws themselves.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Rests on an assumption: The Algorithmic Accountability Act is stalled in a gridlocked Congress.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It should now be clear that the design of technology plays a key role, either in creating discrimination or by reducing it.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Finally, further constraints may arise from affirmative action law, particularly if the fairness intervention goes beyond what is necessary to remedy otherwise unjustified discrimination; this is a complex topic in both the US [22, 145, 252] and the EU legal framework [112, 126].”
What the sources say
- Not matchedcites [22]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [145]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [252]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, a law firm could arguably use a model evaluating orthography and grammar to rank candidates even if this disproportionately disadvantages members of one specific protected group.”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Algorithmic hiring also has the potential to mitigate biases in imperfect human judgments.”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“Job descriptions and organizational communication can play an important role in attracting or repelling specific groups; automation attempts [207] should carefully include fairness evaluations.”
What the sources say
- Not matchedcites [207]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, it is difficult to assess hiring practices for certain vulnerable populations.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Under-representation and sampling biases can be mitigated as a result.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition to these biases, the epistemic validity of prediction targets such as candidates' employability and motivation is questionable.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, unless intent can be shown, many cases of algorithmic discrimination will be argued under the disparate impact prong.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“Finally, disparate impact can be justified if there is a legitimate reason for the practice [250], for example business necessity [18, 249].”
What the sources say
- Not matchedcites [250]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [18] [18] Solon Barocas and Andrew D Selbst. 2016. Big data's disparate impact. California law review (2016), 671-732. -
- Not matchedcites [249]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Not matched
- Probably not a substantial claim. 161
- It may state two separate claims as one. 130
- The quote only partly states this claim. 2
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Representation in Positive Predicted (RPP) [4, 132, 199] is a measure used in noncooperative audits, where the overall population of interest for an algorithm is unknown.”
What the sources say
- Partly supportedcites [4] [4] Muhammad Ali, Piotr Sapiezynski, Miranda Bogen, Aleksandra Korolova, Alan Mislove, and Aaron Rieke. 2019. Discrimination through Optimization: How Facebook's Ad Delivery Can Lead to Biased Outcomes. Proc. ACM Hum. Comput. Interact. 3, CSCW (2019), 199:1-199:30. https://doi.org/10.1145/3359301 - sourceExperimental — accuracy not yet measured
The auditing section describes outside auditors treating unavailable algorithms as black boxes, and the measuring-statistical-significance section (p. 199:13) measures representation as the fraction of an attribute group among people reached by an ad. The paper does not call this measure “Representation in Positive Predicted (RPP)” or say it is used specifically when the overall population of interest is unknown. (LLM, conf=0.80)
- Not matchedcites [132]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [199]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“which prohibit basing hiring decisions on protected attributes [2, 158]”
What the sources say
- Supportedcites [2] [2] Jeremias Adams-Prassl, Reuben Binns, and Aislinn Kelly-Lyth. 2023. Directly discriminatory algorithms. The Modern Law Review 86, 1 (2023), 144-175. - source
The section on the broader scope of direct discrimination describes US disparate treatment as addressing discriminatory intent or explicit classification. The Direct Algorithmic Discrimination section says that using protected characteristics in decisions is a clear-cut case of direct discrimination and discusses selection in job applications on the basis of race or sex. The paper also cautions that EU direct discrimination is broader than US disparate treatment. (LLM, conf=0.88)
- Not matchedcites [158]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This approach probably gained popularity because it aligns with legislation against disparate treatment (US) and direct discrimination (EU), which prohibit basing hiring decisions on protected attributes [2, 158] and because it does not require run-time access to sensitive attributes.”
What the sources say
- Partly supportedcites [2] [2] Jeremias Adams-Prassl, Reuben Binns, and Aislinn Kelly-Lyth. 2023. Directly discriminatory algorithms. The Modern Law Review 86, 1 (2023), 144-175. - sourceExperimental — accuracy not yet measured
The Introduction (page unavailable) says that treating people less favourably because of a protected characteristic constitutes direct discrimination, supporting the legal premise. The Direct Algorithmic Discrimination section (page unavailable) adds that using a proxy can also constitute direct discrimination. Our copy does not establish why proxy reduction gained popularity or whether it avoids run-time access to sensitive attributes. (LLM, conf=0.84)
- Not matchedcites [158]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The prevalent human vs. algorithm evaluation framework is of limited utility; to overcome it, more research on recruitermachine interaction is required [240], including candidate screening models [6] leading to more granular measures.”
What the sources say
- Not matchedcites [240]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Partly supportedcites [6] [6] Jose M Alvarez and Salvatore Ruggieri. 2023. The Initial Screening Order Problem. arXiv preprint arXiv:2307.15398 (2023). - sourceExperimental — accuracy not yet measured
The Introduction (section 1, page unavailable) notes calls to consider the human user's behavior, and the Conclusion (section 5, page unavailable) proposes further work on human decision-making in candidate screening. These support more research on recruiter–machine interaction, but the paper does not say that the prevalent human-versus-algorithm evaluation framework is of limited utility. (LLM, conf=0.83)
Strength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The same model can lead to different results if used in different ways, including the use of different thresholds for classifiers or different cutoffs for rankers.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Salary Difference (SD) [222] is the only measure of this family proposed in the literature.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Most of the fairness measures considered in hiring focus on outcome equity; the field is strongly influenced by the 80% rule (Section 8.2), which is appreciated as a quantitative rule of thumb, but also often criticized [74, 267].”
What the sources say
- Not matchedcites [74]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [267]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:boosted· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This is especially true for measures of accuracy fairness such as BCRD and MID.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For example, the version of DI reported in Equation (3) computes the ratio between the acceptance rates of the worse-off group (min) and the best-off group (max), ignoring prior knowledge of structural inequality and affected communities.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although it is commonly held, especially in fairness research, that disparities against disadvantaged groups should be mitigated, there is less consensus on how to treat algorithms that happen to favor historically disadvantaged communities.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Sensitive AUC (sAUC) [33, 119, 120, 194, 196, 222] is a widely used measure of the information about sensitive attributes stored in (proxy) non-sensitive attributes.”
What the sources say
- Not matchedcites [33]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [119]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [120]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [194]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [196]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [222]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Differently from sAUC and GTR, MIA is an ex-post , model-dependent measure.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In algorithmic hiring, the literature focuses on the amount of information on sensitive attributes 𝑠 encoded by non-sensitive features 𝑥 and the target variable 𝑦 , with the goal of minimizing it.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Departing from this approach, process fairness is operationalized with reference to the information on sensitive attributes encoded in the remaining variables.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Measures of process fairness such as sAUC and MIA detect the presence of sensitive attribute proxies.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“the target variables in Table 4 are very diverse”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The datasets collected in Table 4 are representative of the research on fairness in algorithmic hiring.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Focusing on screening datasets, we find different constructs (e.g. communication skills vs. commitment) annotated by people with different competencies (AMT workers vs. experts) from disparate data sources (YouTube videos vs. mock interviews)”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The datasets collected in Table 4 are representative of the research on fairness in algorithmic hiring.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“datasets with geographical information primarily represent US citizens (6 datasets) and the Netherlands (3 datasets)”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The datasets collected in Table 4 are representative of the research on fairness in algorithmic hiring.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Gender is by far the most common sensitive attribute, overwhelmingly encoded as binary”
Strength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear? Based: unclearIt may state two separate claims as one.
- Rests on an assumption: The datasets collected in Table 4 are representative of the research on fairness in algorithmic hiring.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found no datasets (and consequently no studies) for selection, and only one for evaluation ( IBM HR Analytics )”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The datasets collected in Table 4 are representative of the research on fairness in algorithmic hiring.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“finding one additional dataset (IBM HR Analytics) and no additional article.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Biased employee evaluation has different forms and derives from multiple causes.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“Since discrimination has shaped residential patterns and influenced public transportation, this factor may have disproportionate impacts along racial and ethnic lines [144].”
What the sources say
- Not matchedcites [144]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“This BCF has several concurrent causes, including expectations reinforced by the status quo and lower success in salary negotiation [68, 110, 163].”
What the sources say
- Not matchedcites [68]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [110]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [163]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“This is especially problematic in the hiring domain, where agency is deemed important for leadership positions and it is harder to demonstrate for women without giving an impression of low communion [160].”
What the sources say
- Not matchedcites [160]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“While algorithmic hiring represents an opportunity to mitigate these biases, it also runs the risk of reinforcing and amplifying them, causing harm and hindering trustworthiness [168].”
What the sources say
- Not matchedcites [168]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“When translated into data and dignified with a ground truth status, vertical segregation leads to models that reinforce wage gaps [222] and lack of diversity in high-status positions.”
What the sources say
- Not matchedcites [222]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Whether, and more importantly what types of , algorithmic hiring can be less biased and more beneficial to society than low-tech alternatives currently remains unanswered, to the detriment of trustworthiness.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Prior experience is considered a fundamental predictor of suitability for a given position [101].”
What the sources say
- Not matchedcites [101]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, hiring datasets display measurement errors whose severity varies with protected groups, most critically in target variables related to employability, reflecting current biases in human ratings.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Finally, there are some higher-order effects caused by technological blindspots and biases in external tools integrated into the hiring pipeline.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, biased decisions at early stages result in downstream samples misrepresenting certain groups.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“While algorithmic hiring represents an opportunity to mitigate these biases, it also runs the risk of reinforcing and amplifying them, causing harm and hindering trustworthiness [168].”
What the sources say
- Not matchedcites [168]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Models that predict the likelihood that candidates will accept an offer can reinforce existing wage gaps.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Two key drivers are stereotypes and employee-manager relationships.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Gender and race stereotypes about competence drive people's perception on the workplace and potentially bias supervisor evaluations against female and black employees [116, 229, 233, 242].”
What the sources say
- Not matchedcites [116]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [229]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [233]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [242]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These patterns put some job seekers at a systematic disadvantage based on sensitive attributes such as age, disability, gender, religion or belief, racial or ethnic origin, or sexual orientation.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Elitism in recruiter evaluations favors candidates educated at prestigious institutions who can also list specific extracurricular accomplishments correlated with socioeconomic status [218], further entrenching family status and social class [51, 184].”
What the sources say
- Not matchedcites [218]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [51]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [184]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“For instance, video interviews and resumes contain a wealth of information on gender, race, and other sensitive attributes [63, 69, 189, 218, 222], which can lead algorithms to learn stereotypical associations between sensitive and target variables encoded in the data.”
What the sources say
- Not matchedcites [63]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [69]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [189]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [218]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [222]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Job segregation (i.e. the world today) plays a fundamental role in hiring decisions (i.e. the world tomorrow).”
Strength:boosted· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Predominantly analyzed with respect to binary gender [53, 82], recent studies have also highlighted glass ceiling effects for non-binary workers [62], racial minorities [117], and intersectional identities [273].”
What the sources say
- Not matchedcites [62]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [117]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [273]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, disparities may exist in data sample composition and feature values across sensitive groups that reflect and amplify problematic structural differences in society.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Workplace proximity and availability of reliable commutes can influence job satisfaction [201] and candidate-employer interactions [262], with amplifying effects introduced by technology [142].”
What the sources say
- Not matchedcites [201]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [262]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [142]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“The average job posting yields more than 100 candidates [101, 206].”
What the sources say
- Not matchedcites [101]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [206]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Cultural fit is often considered predictive of a candidate's ability to conform and adapt to the core values and collective behaviors of an organization.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Sensitive attribute proxies allow models to learn and reflect the diverse BCFs described in the following section.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“For example, agency (orientation towards leadership and goal attainment) is stereotypically associated with men, while communion (warmth and propensity for care) is frequently associated with women [72, 118], with far-reaching effects of gender roles and expectations in employment [118, 215].”
What the sources say
- Not matchedcites [72]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [118]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Unfortunately, most work in this space provides partial treatment, often constrained by two competing narratives, optimistically focused on replacing biased recruiter decisions or pessimistically pointing to the automation of discrimination.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The debate on this topic is often polarized between techno-enthusiasm [139, 177] and pessimism [9, 132] due to partial perspectives on a field that is large and complex.”
What the sources say
- Not matchedcites [139]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [177]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [9] [9] Lori Andrews and Hannah Bucher. 2022. Automating Discrimination: AI Hiring Practices and Gender Inequality. Cardozo L. Rev. 44 (2022), 145. -
- Not matchedcites [132]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The field of algorithmic hiring is large and complex.
- Not matched
In the paper
“By one count, there are over 250 Artificial Intelligence (AI) tools for HR on the market [99]”
What the sources say
- Not matchedcites [99]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“This dimension is strongly connected to world views [100, 123], and the extent to which differences in variables between sensitive groups are influenced by measurement errors and unjust social structures.”
What the sources say
- Not matchedcites [100]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [123]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:boosted· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“This approach may be easier to interpret [286], but fails to capture large disparities when acceptance rates are small.”
What the sources say
- Not matchedcites [286]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“For example, an increased representation of a minority group in a company can improve job satisfaction, increase their importance and visibility, and encourage other minority applicants in future hiring rounds.”
What the sources say
- Not matchedcites [155]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [174]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This fact may be reflected in data sets as a lower tenure for these groups, which can be penalized by algorithms trained to maximize tenure in order to reduce hiring costs and retain human capital.”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It should be noted that the platform(s) chosen to run a campaign can introduce a further bias in favor of its predominant demographics; campaigns run on platforms that cater to younger users, for example, are less likely to reach older segments of the population.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Asynchronous video interviews can produce specific patterns from candidates with speech impairment (e.g. short answers) or mismeasure input from candidates with sight impairment (e.g. eye contact), which are judged unfavorably by algorithmic recruitment models [189].”
What the sources say
- Not matchedcites [189]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Fitting models to quantify the influence of a sensitive feature on an outcome variable is typical of situations with limited access to the model(s) responsible for the outcomes. This is a common setting for external noncooperative audits.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Different interpretations have been advanced, including lower risk aversion and the perceived chance of success [110, 122].”
What the sources say
- Not matchedcites [110]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [122]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“On the contrary, job descriptions with unrealistic requirements discourage job seekers who lack one or more requirements, with a repulsive effect that can be stronger for candidates from vulnerable groups [101, 179].”
What the sources say
- Not matchedcites [101]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [179]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“This happens even though advertisers design neutral campaigns and is exacerbated if they target specific attributes [258], increasing the opportunity for bias and opacity at the sourcing stage.”
What the sources say
- Not matchedcites [258]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Explicitly mentioning requirements such as community outreach can signal the posture of an employer and act as a pull factor for minority job seekers [192, 234].”
What the sources say
- Not matchedcites [192]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [234]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“This measure is closely related to equal opportunity [114] and separation [17] and presupposes the availability of a ground-truth variable 𝑦 to condition on:”
What the sources say
- Not matchedcites [114]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [17] [17] Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2019. Fairness and Machine Learning: Limitations and Opportunities . fairmlbook.org. http://www.fairmlbook.org. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Salary negotiation differences between men and women are documented, including in propensity [163] and strategy [110].”
What the sources say
- Not matchedcites [163]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [110]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, while a measure providing a contextualized and accurate operationalization of a construct defined in the law displays normativity, the measure will inherit all the limitations of the underlying law.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Well-connected candidates have an inherent advantage over candidates with lower centrality; this advantage may be amplified by algorithms for link prediction [264].”
What the sources say
- Not matchedcites [264]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“More general examples include position bias that causes underexposure risks for candidates that are not at the top of a ranking [60] and automation bias leading to over-reliance on technology and reduced human oversight [106, 156].”
What the sources say
- Not matchedcites [106]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [156]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These measures are rare in the literature, as they require a broader understanding and modeling of the sociotechnical system around the decision-making algorithm.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This reflects on the visibility and perceived competence of candidates at the different stages of the hiring pipeline; it can be especially difficult to subvert for women due to the social and economic penalties incurred for violating female gender stereotypes [182].”
What the sources say
- Not matchedcites [182]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Measures in this family lack the notion of preferable outcomes and candidate benefits.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, platforms are more likely to have rich profiles and metadata for their common users, while lacking information for less engaged job seekers, who are penalized due to missing data.”
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“Furthermore, people with a migration background are less likely to own a motor vehicle [146].”
What the sources say
- Not matchedcites [146]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“These databases contain a wealth of pre-conviction information, such as arrest data [157] whose validity as a proxy for crime is questionable [97].”
What the sources say
- Not matchedcites [157]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [97]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“With time, this contributes to reducing vertical segregation, with minority employees in key positions shaping company culture, and attracting more candidates with a similar background.”
What the sources say
- Not matchedcites [155]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [174]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Intersectional identities are particularly vulnerable as they are likely to be affected by multiple BCFs.”
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“It is worth noting that the next three sections are based on a systematic review of the literature summarized in Appendix A.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is important to note that these factors are interlocking and overlapping insofar as BCFs are mutually reinforced by the structure of institutions (Section 3.1), individual preferences (Section 3.2), and technological blind spots (Section 3.3).”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This causes an overrepresentation of the former in the training data, leading to rich-get-richer dynamics in job platforms [185].”
What the sources say
- Not matchedcites [185]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This difference may be related to gender roles with respect to household and childcare responsibilities.”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Social networks topology influences job seekers' awareness of openings [96], and their likelihood of being successfully screened by recruiters due to a successful referral [136, 284].”
What the sources say
- Not matchedcites [96]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [136]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [284]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Accessibility issues and ableist norms can discourage disabled people from job applications [226] and tilt evaluations against them [245, 246].”
What the sources say
- Not matchedcites [226]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [245]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [246]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“Off-the-shelf algorithms from these domains integrated into hiring pipelines [141] are likely to underserve minority candidates for feature extraction and negatively affect algorithms that are based on these feature.”
What the sources say
- Not matchedcites [141]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that minority job seekers may suffer a lower quality of service, leading to disengagement and triggering a negative feedback loop by iteratively lowering training representation, quality of service, and engagement.”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Background check tools allow employers to obtain additional information on candidates from public records [244].”
What the sources say
- Not matchedcites [244]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“They are all conditional on the target variable 𝑦 , inheriting the biases encoded in the ground truth.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Similar criticism can be moved to accuracy fairness, with the additional limitation that it is less interpretable and less aligned with the desiderata of job seekers.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Dedicated proxy reduction approaches (Section 5) can mitigate the resulting risk of discrimination.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“this method is based on the observation that most intrinsically gendered information in word embeddings, such as the difference between the vectors for 'mother' and 'father', is contained within a small gender subspace.”
What the sources say
- Not matchedcites [32]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Not matched
In the paper
“In terms of outcome fairness, Name Decorrelation is found to improve TPRD and RMS with respect to gender but not ethnicity.”
What the sources say
- Not matchedcites [119]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“this approach achieves mitigation on a held-out test set during development, but provides no guarantee at deployment.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Balanced sampling, for example, can counter the effect of under-representation in training sets caused by factors such as horizontal and vertical job segregation.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“It consists of short resumes containing a binary gender indicator (he/she) and a two-sentence description of job skills for IT, finance, and administration.”
What the sources say
- Not matchedcites [283]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Impact fairness can highlight nuanced aspects of hiring beyond selection rates; SD, for instance, captures wage gaps and salary negotiation differences prompting further analysis into remuneration packages.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“While this method can mitigate large outcome disparities, it does not produce convincing improvements when the inequity is less significant [33, 119, 120].”
What the sources say
- Not matchedcites [33]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [119]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [120]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“The dataset comprises approximately 900,000 resumes (without names, emails, and URLs) in the historical hiring records of eight IT firms based in the US, relevant to just over 6,000 job postings from the IT sector.”
What the sources say
- Not matchedcites [196]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To exemplify, a combination of job segregation, work gaps, and disparate performance of language processing tools, even if relatively weak on their own, may trigger a very low (unfair) value of DI when they compound.”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“These are likely two key factors explaining why post-processing is less popular and why hybrid approaches combining different types of fairness interventions, e.g. mitigating via preand post-processing, remain under-explored [11].”
What the sources say
- Not checked yetcites [11] [11] Adam Mehdi Arafan, David Graus, Fernando P Santos, and Emma Beauxis-Aussalet. 2022. End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates. In 2nd Workshop on Recommender Systems for Human Resources, RecSys-in-HR 2022 . CEUR-WS, 1-8. -
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“notably, their sAUC is on par with adversarial methods explicitly trained to predict gender and ethnicity,”
What the sources say
- Not matchedcites [120]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe quote only partly states this claim.
- The authors state this as an assumption: Face data contain enough information on sensitive features such as gender and race that successful debiasing can be achieved without explicit sensitive attribute information.
- Not matched
In the paper
“DPG Resumes [222] contains over 10 million vacancies (including salary range, working hours, and job descriptions) and just under 1 million resumes augmented with job categories of interest and gender inferred from first names.”
What the sources say
- Not matchedcites [222]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“Bias in Bios , [63] is composed of textual biographies written in English and extracted from the Common Crawl dataset.”
What the sources say
- Not matchedcites [63]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Representational fairness (GBS) captures biases in representations; when applied to job descriptions, extreme values highlight stereotypically gendered language, signaling a risk of culture-based avoidance.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Having access to sensitive attributes at runtime enables precise and reliable interventions, whereas strategies that do not rely on this data must be continuously monitored to ensure they are meeting their intended goals.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Proxy reduction methods (e.g. decorrelation, adversarial inference) target sensitive attribute proxies, reducing the overreliance of models on sensitive information.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: Resumes and biographies contain strong proxies for sensitive attributes such as race and gender, including names and addresses.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This approach represents an intermediate view on conditional discrimination: it prohibits inter-group discrimination based on a specific feature, while allowing it as a basis for intra-group discrimination.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Visual signals have been removed from several products [175] because they inevitably encode sensitive information such as race and gender while lacking a solid foundation to justify their use in the hiring domain.”
What the sources say
- Not matchedcites [175]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Even if found to be accurate and fair in a specific evaluation, hiring algorithms based on face analysis are unlikely to predictably generalize and maintain accuracy or fairness under variable conditions, or at least they are less likely to do so than algorithms based on more established data modalities and better-understood correlations with job performance.”
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Finally, output re-ranking can mitigate the joint effect of different bias conducive factors.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“The effectiveness of Face Decorrelation for outcome fairness is more nuanced, showing positive effects on DI for very unfair models based on the video modality and limited effects on more equitable systems based on language and audio.”
What the sources say
- Not matchedcites [120]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: Face data contain enough information on sensitive features such as gender and race that successful debiasing can be achieved without explicit sensitive attribute information.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“this result suggests that targeting this type of process fairness can improve outcome fairness in highly imbalanced situations, while only providing limited benefits in situations with lower inequity.”
What the sources say
- Not matchedcites [119]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [120]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Gender and race, for example, are considered sensitive attributes due to the recurrent structural disadvantages incurred by women and black people.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, specific data modalities, such as text and vision, facilitate specialized methods like subspace projection and face decorrelation, which function without sensitive attribute information about data subjects.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Group norming seems applicable to counter systematic biases encoded in input features against protected groups, such as biased employee evaluations.”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Conversely, pre-processing and in-processing approaches, such as importance-based scraping and adversarial inference, require access to sensitive attributes only during training, not testing.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“No single measure provides a complete picture. Different fairness flavors have complementary strengths and weaknesses.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Indeed, outcome fairness is based on a narrow view of algorithmic hiring as a single decision point abstracted away from its context, where equity is purely a function of algorithmic estimates and (sometimes) their similarity to target variables approximating a ground truth.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“while Peña et al. [199] demonstrate SKL improvements in a multimodal setting based on synthetic resume ranking .”
What the sources say
- Not matchedcites [199]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:boosted· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Impact fairness quantifies the downstream impacts of algorithmic outcomes on different populations; modeling important aspects of the surrounding socio-technical system, it goes beyond brittle algorithmic abstraction.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“It is composed of applications, i.e. pairs of resumes and job postings for engineers (software and hardware) and technical scientists.”
What the sources say
- Not matchedcites [58]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“It is worth noting that post-processing explicitly takes into account sensitive attributes to change algorithmic outcomes for job candidates, which may be critical for disparate treatment and affirmative action (US) as well as direct discrimination and positive action (EU) [22, 112].”
What the sources say
- Not matchedcites [22]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [112]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“Both variants of Adversarial Removal (NS, MSE) are shown reduce the sensitive information encoded in latent representations:”
What the sources say
- Not matchedcites [120]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe quote only partly states this claim.
- The authors state this as an assumption: Face data contain enough information on sensitive features such as gender and race that successful debiasing can be achieved without explicit sensitive attribute information.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Both sAUC and GTR are ex-ante measures, i.e. they can be computed from the data before the final model is trained; therefore, the granularity dimension does not apply to them.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Geyik et al. [103] decouple their post-processing method from specific model choices and properties of input data, which means their method can naturally scale across the different (ranking) systems at LinkedIn”
What the sources say
- Not matchedcites [103]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“datasets that target employee tenure and performance, such as Walmart Employees and IBM HR Analytics , signal an interest from companies in understanding the factors that predict future productivity and loyalty”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Artificial target scores are generated for each CV, as a linear combination of numerical features; biased scores are derived from these with an ethnicity- and gender-dependent additive penalty emulating biases in the data.”
What the sources say
- Not matchedcites [199]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This can be especially problematic when using conditional fairness measures whose very definition hinges on this so-called ground truth”
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
In the paper
“GPT-4 is found to more frequently use female pronouns in reference letters generated for female-dominated occupations (such as nannies), and male pronouns for male-dominated occupations (such as plumbers) [35]”
What the sources say
- Not matchedcites [35]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“This challenge may explain in part the popularity of disparate impact as a fairness metric, due to its ease of interpretation across a broad and diverse stakeholder group, or the popularity of complying with the EEOC's 80% rule [77] as a fairness target (adopted e.g. by Pymetrics [271]), as it appears, at least superficially, a legally grounded target”
What the sources say
- Not matchedcites [77]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [271]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“this allows these methods to be deployed and run in isolation from production systems in controlled batch scenarios, which can be another important practical benefit”
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“a third family of bias mitigation methods that can operate without requiring access to sensitive attributes at all are the rule-based approaches described above, which are commonly used for mitigating bias in textual data such as job descriptions or resumes”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Defining fairness and formulating an intervention target is not a one-off task, as algorithmic hiring components will be deployed in different, product-specific contexts and stages of the hiring pipeline”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Geyik et al. [103] find their DetGreedy method improves their fairness metric, with no significant impact on utility”
What the sources say
- Not matchedcites [103]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Significantly, the Equal Employment Opportunity Commission (EEOC) issues nonbinding, but practically important guidelines to interpret Title VII.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Examples of these bias mitigation methods used by practitioners include adversarial inference [222], and re-balancing training data with synthetic data generation [11]”
What the sources say
- Not matchedcites [222]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [11] [11] Adam Mehdi Arafan, David Graus, Fernando P Santos, and Emma Beauxis-Aussalet. 2022. End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates. In 2nd Workshop on Recommender Systems for Human Resources, RecSys-in-HR 2022 . CEUR-WS, 1-8. -
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These methods rely solely on domain and task-specific gazetteers or dictionaries, and hence can be used when access to sensitive attributes of individuals is not available or desirable”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This type of adversarial bias mitigation can, however, be applied over a wider set of data modalities”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the challenge in defining fairness and formulating intervention targets in algorithmic hiring is exacerbated by the multi-stakeholder nature of the hiring domain”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“pre-processing bias mitigation methods may prove more useful, as they (only) require access to sensitive attributes at training time, and can operate on attributes even when available for only a subset of job seekers”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“tabular data is a common data modality in algorithmic hiring and machine learning more broadly”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“the validity of these estimates and their connection with job performance has been called into question [19, 214]”
What the sources say
- Not checked — no full text of the sourcecites [19] [19] Murray R Barrick and Michael K Mount. 1991. The big five personality dimensions and job performance: a metaanalysis. Personnel psychology 44, 1 (1991), 1-26. - source
- Not matchedcites [214]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“It comprises AVIs with 154 students from Utrecht University carrying out a mock interview with a fictitious oil company.”
What the sources say
- Not matchedcites [139]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For classification tasks, getting started with bias mitigation is relatively straightforward, as there exist several open source or otherwise freely available software packages and libraries, as mentioned above, that provide different implementations of bias measurement methods and mitigation algorithms, specifically designed for classification tasks”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“they are consistent with previous research reporting that most efforts to improve fairness in artificial intelligence are influenced by the Global North [187, 221], particularly the US [135], and focus on English language resources [210]”
What the sources say
- Not matchedcites [187]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [221]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [135]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [210]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Once data modality, task, infrastructural and technical choices, and access to sensitive data are set, one important practical challenge is that of defining 'fairness' and formulating the intervention target of a bias mitigation method”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Importantly, disparate treatment requires not only an adverse action based on a protected attribute, but also the proof of intent on the part of the discriminating individual - unlike the EU variety of direct discrimination [254].”
What the sources say
- Not matchedcites [254]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“Candela et al. [39] present the (evolving) framework used at LinkedIn that guides definitions and operationalizations of AI fairness across their different (types of) products”
What the sources say
- Not matchedcites [39]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Adversarial inference methods, which have been applied to a variety of data modalities and tasks, even require training multiple neural networks simultaneously, which means they can incur substantially higher costs compared to their non-mitigated counterparts”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The two most common tasks in algorithmic hiring are classification and ranking”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Developers of high-risk AI systems must, for instance, ensure that the training data are appropriate and do not lead to unlawful discrimination.”
What the sources say
- Not matchedcites [84]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In general, post-processing approaches offer an advantage in terms of system integration, but they should be applied with care due to antidiscrimination law”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Ranking is a common task in the sourcing and screening stages of hiring, performed by search engines or recommender systems”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“video-based systems tend to yield more disadvantageous effects for protected groups, also due to the strong encoding of sensitive information”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIt may state two separate claims as one.
In the paper
“SHL Interviews [231] is a proprietary AVI dataset with more than 5,000 videos from 810 real job seekers from the US, UK, India, and Europe answering behavioral and domain knowledge questions.”
What the sources say
- Not matchedcites [231]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“EU law distinguishes two categories of prohibited discrimination: direct and indirect”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“incoming regulation proposals that prohibit inference of emotions or intentions from facial data in workplace contexts”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“These types of methods are widely available in open-source bias mitigation software packages such as the Fairlearn Python package [268] or the AI Fairness 360 toolkit (available in Python and R) [21]”
What the sources say
- Not matchedcites [268]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [21] [21] Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, and Yunfeng Zhang. 2018.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These mitigation strategies tend to be technically straightforward to implement, as they can be developed as standalone components and need not be integrated in complex algorithmic hiring pipelines”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“alignment with the company's goals and values, interpretability (or ability to explain), and collaboration across different stakeholders were mentioned as key factors in guiding the eventual target distribution”
What the sources say
- Not matchedcites [103]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“algorithmic efficiency and computational costs will vary across bias mitigation methods in nature and magnitude, with simple rule-based scraping methods that involve dictionary look-ups on the cheap end of the spectrum, and bias mitigation methods that involve re-training LLMs at the resource-intensive end [167]”
What the sources say
- Not matchedcites [167]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This also means that such a method can be developed and deployed as a standalone component or micro-service, which decreases development time, effort, and alignment, when compared to pre-processing or in-processing methods that may need to be designed and integrated in complex algorithmic hiring pipelines”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Companies interested in algorithmic hiring solutions pick one or more of these variables, balancing different priorities such as efficiency and quality of hire, with unpredictable effects on algorithmic fairness, a phenomenon called multi-target multiplicity [266]”
What the sources say
- Not matchedcites [266]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“Student Interviews [33] consists of video interviews with 733 upper-level undergraduate students,”
What the sources say
- Not matchedcites [33]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“There is a proposal for an AI Act in the EU, with many requirements for 'high-risk' systems, including algorithmic systems for HR [84].”
What the sources say
- Not matchedcites [84]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the prevalence of classification-based bias metrics in algorithmic hiring (discussed in more detail in Section 4) is reflected in the aforementioned open source packages, which means that measuring and mitigating bias in ranking systems tends to follow the practice of re-purposing classification methods through, e.g., thresholding rankings”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“access to sensitive attributes may be at odds with privacy regulations such as the GDPR”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Direct discrimination is always prohibited, with a few narrowly defined and specific examples”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Many other sensitive attributes (e.g., religious beliefs) will not be easily available, and will have to be explicitly requested for bias measurement and mitigation purposes”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Textual data is a common data modality encountered in the sourcing stage, typically in the form of textual CV or job description data, or in the screening stage, e.g. transcripts of video interviews”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“different data modalities enable different bias mitigation strategies”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Mitigating bias in tabular data can be done through pre-processing approaches that directly intervene on the features, e.g. by removing features that are highly correlated with sensitive attributes via importance-based scraping, or increasing the representation of vulnerable populations in training sets with balanced sampling”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the sourcing or screening stages, depending on the intervention target, outcome fairness may come at the cost of utility; this can happen, for example, when optimizing for gender parity in heavily male or female-dominated industries”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The proposals in the AI Act also contain a new exception to the GDPR, to enable the use of sensitive data for debiasing algorithmic systems.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Disparate impact, in turn, closely resembles indirect discrimination. Intent is not required [249].”
What the sources say
- Not matchedcites [249]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“the desired distribution of the ranking is set to be equivalent to that of the underlying population of job seekers”
What the sources say
- Not matchedcites [103]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedSingle caseTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Many target variables can seem reasonable at face value; therefore, initial data curation and design choices have a prominent role”
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
In the paper
“they reflect the importance of both countries in the recruitment industry”
What the sources say
- Not checked yetcites [3] [3] Ifeoma Ajunwa. 2019. The paradox of automation as anti-bias intervention. Cardozo L. Rev. 41 (2019), 1671. -
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In its guidelines, the EEOC suggested that such a disadvantage usually occurs if the chance of a member of the protected group being positively evaluated is 80% or less than that of a member of the privileged group (so-called 80% rule or 4/5 rule) [77].”
What the sources say
- Not matchedcites [77]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“due to video-based algorithms unreliability in the face of varying conditions”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For indirect discrimination, the law focuses on the effects of a practice; the intention of the alleged discriminator is not relevant”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“desired utility levels are just one dimension, and often not the most restrictive”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“some sensitive attributes, such as age and gender, may have to be recorded in the hiring process for identification purposes, and can hence be assumed to be available for all job seekers”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“ChaLearn First Impression [79] contains 10,000 YouTube video clips of people facing a camera and speaking in English.”
What the sources say
- Not matchedcites [79]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“collecting the required data (e.g. constructing task-specific dictionaries) leans heavily on domain and context-specific knowledge, which is typically available to practitioners in the HR domain”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For all these acts, US law distinguishes between two fundamental types of discrimination: disparate treatment and disparate impact.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Title VII of the Civil Rights Act of 1964 constitutes a federal law prohibiting employment discrimination based on race, color, religion, sex, and national origin.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“this reflects the length of hiring pipelines and the diversity of data journeys”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Some algorithms are explicitly trained and evaluated to 'predict the competency scores candidates would have been given by trained human reviewers' [189], inheriting individual biases from recruiters.”
What the sources say
- Not matchedcites [189]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“In force since the year 2020, the law requires employers to put candidates on notice, and obtain their consent, before subjecting their video interview to AI analysis. Candidates may request deletion, and employers that rely solely on AI need to collect and report candidates' race and ethnicity.”
What the sources say
- Not matchedcites [131]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“For each included article, we perform forward and backward snowballing [272], pre-filtering article titles with a ' hiring ∧ fairness ' query and applying the inclusion criteria in (3).”
What the sources say
- Not matchedcites [272]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To ensure a broad coverage of the scientific literature centered on CS, we leverage three scholarly search engines: IEEE Xplore, ACM Digital Library, and Google Scholar.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To guarantee precision, we manually analyze each article and only select the ones that treat fairness in algorithmic hiring from a quantitative perspective, i.e. performing a fairness audit or introducing a novel method.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Some of these measures are completely symmetric and consider advantages for vulnerable and privileged groups as equally problematic (which is, however, generally required by the law).”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Algorithms cloaked in objectivity can promote bias while targeting and legitimizing ill-defined quantities.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Effective and equitable hiring can result from a fruitful interaction between technology and recruiters, leveraging complementary strengths.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Data protection law restricts these analyses and should consider exceptions for algorithmic fairness, as now suggested in the EU AI Act.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The exact contours of legally compliant algorithmic fairness remain contested.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Finally, we invite special caution in the development of predictive models for job performance and tenure, due to a risk of exploitation for termination decisions [285]; such an application of algorithms raises even stronger ethical and social concerns, which are only beginning to be discussed [223].”
What the sources say
- Not matchedcites [285]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [223]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:boosted· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is worth highlighting that improving fairness does not require completely removing bias.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The EU AI Act may offer a (limited) solution by allowing certain types of sensitive data processing to remove biases in high-risk scenarios.”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is because non-technological factors have been shown, on occasion, to strongly influence decisions and amplify technological bias, as we will explore in this section.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Algorithmic hiring benefits from, and contributes to, fairness and anti-discrimination work, as the previous sections have shown.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On the other hand, these changes may be difficult to detect and quantify.”
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We use the query algorithmic ∧ hiring ∧ fairness on article titles, where each term is expanded as follows:”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Most fairness measures are focused on narrow algorithmic outcomes, neglecting the wider socio-technical context around these algorithms.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To ensure high recall, we consider the top 100 results.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These companies have invested heavily in their digital hiring technology as suppliers or customers, so are highly motivated to retain it, even once there is no longer a strong need for it.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, important sensitive attributes are missing from the data, making it difficult or impossible to evaluate algorithms for specific vulnerable groups.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In general terms, an algorithmic system does not cause indirect discrimination or disparate impact if it pursues legitimate aims through appropriate means .”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“During the COVID-19 pandemic, delivery companies such as Amazon, Deliveroo, and DoorDash rolled out algorithmic recruitment systems to avoid the danger of viral contagion in their HR teams, as well as experiment with a new technology that had the potential to save millions of dollars in HR bills [281].”
What the sources say
- Not matchedcites [281]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In practice, binding regulation shapes algorithmic development more than ethical guidelines or self-regulation.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is further complicated by changing incentive structures, by the complexity of handling sensitive data, and by the frequent delay between decisions and feedback in hiring.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, in the presence of an exogenous shock, such as the COVID-19 pandemic, workplaces are heavily affected, with large consequences on hiring and recruitment.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Apart from the acts mentioned, federal legislation specifically addressing discrimination in AI systems is unlikely to emerge anytime soon.”
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The Algorithmic Accountability Act is stalled in a gridlocked Congress.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Individual fairness measures can surface problematic situations for individuals that may go unnoticed when studying group fairness.”
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“More data entails more scrutiny and reflection, which can inform organizational frameworks, such as Diversity, equity, and inclusion , and scholarly fields, such as applied psychology and economics.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Finally, we consider articles and datasets presented in related surveys [90, 91, 173, 280] or published at dedicated venues”
What the sources say
- Not matchedcites [90]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [91]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [173]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [280]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Here, the answers depend on affirmative action (US) and positive action (EU) law, which are currently in flux after the US Supreme Court's decision against Harvard's and UNC's affirmative action programs.”
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Although hiring technology can be more ethical than humans and reduce bias in decision-making [138], it often reinforces bias and results in distinctly non-ethical outcomes.”
What the sources say
- Not matchedcites [138]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although clear guidance on algorithmic hiring is currently missing, precise requirements set out in future regulation and case law, informed by research and practice on fair algorithmic hiring, have the potential to influence the industry positively and profoundly.”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although hiring technology can be more ethical than humans and reduce bias in decision-making [138], it often reinforces bias and results in distinctly non-ethical outcomes.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Data and research are constrained by a dual tension with the privacy of data subjects, on one side, and the intellectual property (IP) of companies, on the other side.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, fairness for employers is currently missing; two-sided platforms would be well-positioned to study the performance of their algorithms across employers, devoting special attention to small businesses.”
Strength:hedged· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Zooming out from single-algorithm evaluations, it is worth noting that outcome fairness at every stage does not guarantee fair outcomes for job seekers throughout the hiring pipeline.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Algorithmic fairness research is contributing additional analysis into hiring practices from a perspective of diversity and non-discrimination.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, special categories of personal data are often lacking and difficult to process, particularly in the EU under the current data protection law.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Promising technological approaches include multiparty computation [143], sample-level estimators [89], and noise injection mechanisms [137].”
What the sources say
- Not matchedcites [143]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [89]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [137]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Exploratory policies based on partially stochastic mechanisms can provide new information in repeated decision-making scenarios.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is most likely due to concerns of infringing regulation on disparate treatment and direct discrimination.”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Strong shocks in the hiring domain are an issue for data representativeness more broadly. Fresh data becomes necessary.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Research on fairness in algorithmic hiring is based on data with reduced geographical and linguistic coverage.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
In the paper
“From July 5, 2023, NYC Local Law 144 applies to employers using AI to substantially assist or replace discretionary decision-making in hiring. They are required to conduct and publish impartial bias evaluations by an independent auditor.”
What the sources say
- Not matchedcites [186]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“For example, HR professionals are better suited to assess special cases and operate under changing conditions [166].”
What the sources say
- Not matchedcites [166]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Finally, it is worth noting that the discovery of patterns that predict job performance for hiring can open the way to models for termination decisions.”
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Fair and trustworthy algorithms can lead to a positive form of automation bias and attract minority candidates.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Algorithmic hiring can reduce certain disparities and trigger deflating feedback loops across bias conducive factors.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Most of the fairness approaches developed so far are restricted to proxy reduction or removal, neglecting a wealth of solutions developed by the algorithmic fairness community.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Algorithmic fairness can conflict with privacy and non-discrimination doctrine.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The social, technological, and legal landscape around algorithmic hiring is rapidly evolving; algorithmic fairness has become a necessary component for both business-as-usual product development and frontier research.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The recent pandemic necessitated social distancing, but once this danger had passed, non-technological hiring procedures could be reintroduced.”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Job performance is famously difficult to define and measure, let alone predict [220, 263].”
What the sources say
- Not matchedcites [220]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [263]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“One reason they were able to do this with ease was due to the regulatory environment, which was laxer than usual due to the ongoing emergency conditions.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
Employers are adopting algorithmic hiring technology throughout the recruitment pipeline.
“Employers are adopting algorithmic hiring technology throughout the recruitment pipeline.”
The paper relies on this without support.
Algorithmic fairness is especially applicable in algorithmic hiring due to its high stakes and structural inequalities.
“Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities.”
The paper relies on this without support.
The field of algorithmic hiring is large and complex.
“due to partial perspectives on a field that is large and complex.”
The authors state this as an assumption.
Face data contain enough information on sensitive features such as gender and race that successful debiasing can be achieved without explicit sensitive attribute information.
“This approach is based on the key assumption that face data contain enough information on sensitive features such as gender and race, so that successful debiasing can be achieved without explicit sensitive attribute information.”
The paper relies on this without support.
Resumes and biographies contain strong proxies for sensitive attributes such as race and gender, including names and addresses.
“Resumes and biographies contain strong proxies for sensitive attributes such as race and gender, including names and addresses.”
The paper relies on this without support.
The datasets collected in Table 4 are representative of the research on fairness in algorithmic hiring.
“Table 4. Datasets used in research on fairness in algorithmic hiring.”
The paper relies on this without support.
The European Convention on Human Rights and the Charter of Fundamental Rights of the European Union protect the right to non-discrimination in Europe.
“The right to non-discrimination is protected as a human right in Europe by the European Convention on Human Rights (1950) and the Charter of Fundamental Rights of the European Union (2020).”
The paper relies on this without support.
The word 'disadvantage' in indirect discrimination must be interpreted in a wide way.
“The word disadvantage must be interpreted in a wide way.”
The paper relies on this without support.
The organization using an algorithmic system is responsible if the system discriminates illegally.
“The organization is also responsible if it uses an algorithmic system provided by, for instance, a company.”
The paper relies on this without support.
The Algorithmic Accountability Act is stalled in a gridlocked Congress.
“The Algorithmic Accountability Act is stalled in a gridlocked Congress.”
The paper relies on this without support.
Previous experience is a preeminent feature for assessing candidates.
“Previous experience is a preeminent feature for assessing candidates.”
The paper relies on this without support.
Current job segregation exists.
“In conjunction with current job segregation, this means that the most important features are inevitably skewed against historically disadvantaged groups.”
Citations
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
THE BIG FIVE PERSONALITY DIMENSIONS AND JOB PERFORMANCE: A META‐ANALYSIS
BARRICK, M. R., & MOUNT, M. K. (1991) · Personnel Psychology · doi:10.1111/j.1744-6570.1991.tb00688.x · Cited by 8,689
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00482 · Cited by 7,781
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220
A statistical interpretation of term specificity and its application in retrieval
Jones, K. S. (2021) · J. Documentation · doi:10.1108/eb026526 · Cited by 5,541
Bertrand, M., & Mullainathan, S. (2004) · American Economic Review · doi:10.1257/0002828042002561 · Cited by 4,504
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208
Ekman, P., & Friesen, W. V. (1975) · Cited by 3,569
Training Language Models to Follow Instructions with Human Feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-2011 · Cited by 3,117
Flagged first, then by citations
Research works printed but not linked: 25
- Julia Angwin, Noam Scheiber, and Ariana Tobin. 2017. Dozens of companies are using facebook to exclude older workers from job ads. Machine Bias. ProPublica, New York, NY. Retrieved from https://www.propublica.org/article/facebook-ads-age-discrimination-targeting
- Adam Mehdi Arafan, David Graus, Fernando P. Santos, and Emma Beauxis-Aussalet. 2022. End-to-end bias mitigation in candidate recommender systems with fairness gates. In Proceedings of the 2nd Workshop on Recommender Systems for Human Resources (RecSys-in-HR ’22). CEUR-WS, 1–8.
- Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2019. Fairness and Machine Learning: Limitations and Opportunities. Retrieved from http://www.fairmlbook.org
- Miranda Bogen and Aaron Rieke. 2018. Help Wanted: An Examination of Hiring Algorithms, Equity, and Bias. Technical Report. Upturn.
- 10.2308/accr-10099 · DOI
- Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, and Adam Tauman Kalai. 2016. Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016. Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett (Eds.), 4349–4357. Retrieved from https://proceedings.neurips.cc/paper/2016/hash/a486cd07e4ac3d270571622f4f316ec5-Abstract.html
- Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the Conference on Fairness, Accountability and Transparency (FAT ’18). Sorelle A. Friedler and Christo Wilson (Eds.), Vol. 81, PMLR, 77–91. Retrieved from http://proceedings.mlr.press/v81/buolamwini18a.html
- Ian Burke, Robin Burke, and Goran Kuljanin. 2021. Fair candidate ranking with spatial partitioning: Lessons from the SIOP ML competition. In Proceedings of the 1st Workshop on Recommender Systems for Human Resources (RecSys in HR ’21) Co-located with the 15th ACM Conference on Recommender Systems (RecSys ’21), Vol. 2967.
- Bo Cowgill. 2018. Bias and Productivity in Humans and Algorithms: Theory and Evidence from Resume Screening. Columbia Business School, Columbia University, Vol. 29.
- Harrison Edwards and Amos J. Storkey. 2016. Censoring representations with an adversary. In Proceedings of the 4th International Conference on Learning Representations (ICLR ’16). Yoshua Bengio and Yann LeCun (Eds.). arXiv:1511.05897. Retrieved from http://arxiv.org/abs/1511.05897 · arXiv
- Ben Eubanks. 2022. Artificial Intelligence for HR: Use AI to Support and Develop a Successful Workforce. Kogan Page Publishers.
- A. S. Fokkens, C. J. Beukeboom, and E. Maks. 2018. Leeftijdsdiscriminatie in vacatureteksten: Een geautomatiseerde inhoudsanalyse naar verboden leeftijd-gerelateerd taalgebruik in vacatureteksten: Rapport in opdracht van het College voor de Rechten van de Mens.
- Joseph Fuller, Manjari Raman, Eva Sage-Gavin, and Kristen Hines. 2021. Hidden Workers: Untapped Talent. Technical Report. Harvard Business School.
- Jobvite. 2021. 2021 Recruiter Nation Report. Technical Report. Retrieved from https://www.jobvite.com/lp/2021-recruiter-nation-report/
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- Nicolas Kayser-Bril. 2023. LinkedIn Automatically Rates “Out-of-Country” Candidates as “Not Fit” in Job Applications. Technical Report. AlgorithmWatch.
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- Pauline T. Kim. 2022. Race-aware algorithms: Fairness, nondiscrimination and affirmative action. California Law Review 110 (2022), 1539.
- Eve A. Levin. 2018. Gender-normed physical-ability tests under Title VII. Columbia Law Review 118, 2 (2018), 567–604.
- ORCAA. 2020. Description of Algorithmic Audit: Pre-built Assessments. Technical Report. Retrieved from https://techinquiry.org/HireVue-ORCAA.pdf
- Mary-Ann Russon. 2020. Uber sued by drivers over ’automated robo-firing’. BBC News 26 (2020).
- Keith E. Sonderling, Bradford J. Kelley, and Lance Casimir. 2022. The promise and the peril: Artificial intelligence and employment discrimination. University of Miami Law Review 77 (2022), 1.
- Elmira van den Broek, Anastasia V. Sergeeva, and Marleen Huysman. 2019. Hiring algorithms: An ethnography of fairness in practice. In Proceedings of the 40th International Conference on Information Systems (ICIS ’19). Helmut Krcmar, Jane Fedorowicz, Wai Fong Boh, Jan Marco Leimeister, and Sunil Wattal (Eds.), Association for Information Systems. Retrieved from https://aisel.aisnet.org/icis2019/future_of_work/future_work/6
- Sarah-Jane van Els, David Graus, and Emma Beauxis-Aussalet. 2022. Improving fairness assessments with synthetic data: A practical use case with a recommender system for human resources. In Proceedings of the 1st International Workshop on Computational Jobs Marketplace (CompJobs ’22), 5 pages.
- Hilde Weerts Miroslav Dudík Richard Edgar Adrin Jalali Roman Lutz and Michael Madaio. 2023. Fairlearn: Assessing and improving fairness of AI systems 8 pages. Retrieved from http://jmlr.org/papers/v24/23-0389.html
Cited by
Sorted by citations
A Challenge-based Survey of E-recruitment Recommendation Systems
Mashayekhi, Y., Li, N., Kang, B., Lijffijt, J., & Bie, T. D. (2024) · ACM Computing Surveys · doi:10.1145/3659942 · Cited by 76
Sony, M. M. A. A. M., Amin, M. B., Ashraf, A., Islam, K. M. A., Debnath, N. C., & Debnath, G. C. (2025) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2025.102082 · Cited by 32
Lazy Data Practices Harm Fairness Research
Simson, J., Fabris, A., & Kern, C. A. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658931 · Cited by 13
Self-citationAlgorithmic bias in image-generating artificial intelligence: prevalence and user perceptions
Messingschlager, T. V., & Appel, M. (2025) · Information Communication & Society · doi:10.1080/1369118x.2025.2584146 · Cited by 21
AI in Everyday Life: How Algorithmic Systems Shape Social Relations, Opportunity, and Public Trust
Ayeni, O. B., Musinguzi-Karamukyo, I., Onibalusi, O. T., & Omigbodun, O. M. (2026) · Societies · doi:10.3390/soc16020059 · Cited by 5
Metadata
- Authors
- Fabris, A., Baranowska, N., Dennis, M. J., Graus, D., Hacker, P., Saldivar, J., Zuiderveen Borgesius, F., & Biega, A. J.
- Affiliations
- Max Planck Institute for Security and Privacy (DE)
- Radboud University Nijmegen (NL)
- Eindhoven University of Technology (NL)
- Randstad, Diemen, The Netherlands
- European University Viadrina (DE)
- Universitat Pompeu Fabra (ES)
- Radboud University, Nijmegen, The Netherlands
- ORCID
- Venue
- ACM Transactions on Intelligent Systems and Technology
- Published
- 2025-01-03
- Volume
- 16
- Issue
- 1
- Pages
- 1-54
- ISSN
- 2157-6904 (ISSN-L)
- 2157-6912
- OpenAlex
- W4402747641
- Semantic Scholar
- 768a0b0c53681a2fe901660dc1596d4dc11cb125
- Cited by
- 117
- References
- 213
- Citations by year
- 2026: 75 · 2025: 39
- FWCI
- 48.63
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Transactions on Intelligent Systems and Technology (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by-sa)
- UvA-DARE (University of Amsterdam) (submittedVersion, cc-by)
- Radboud Repository (Radboud University) (submittedVersion)
- ArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/) (submittedVersion, cc-by)
- TU/e Research Portal (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- FINDHR (101070212)
- Alexander von Humboldt-Stiftung
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
- Keywords
- automated hiring, algorithmic fairness, algorithmic bias, recruitment automation, discrimination, fairness metrics, bias mitigation, technology governance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-30, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-30 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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