Investigating the Impact of Gender on Rank in Resume Search Engines
Open accessAbstract
In this work we investigate gender-based inequalities in the context of resume search engines, which are tools that allow recruiters to proactively search for candidates based on keywords and filters. If these ranking algorithms take demographic features into account (directly or indirectly), they may produce rankings that disadvantage some candidates. We collect search results from Indeed, Monster, and CareerBuilder based on 35 job titles in 20 U. S. cities, resulting in data on 855K job candidates. Using statistical tests, we examine whether these search engines produce rankings that exhibit two types of indirect discrimination: individual and group unfairness. Furthermore, we use controlled experiments to show that these websites do not use inferred gender of candidates as explicit features in their ranking algorithms.
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Uncontrolled biases in recommender systems can lead to uneven distribution of opportunities and resources, exacerbating echo chamber or filter bubble effects.
“Usually, a biased system generates the biased output, and the uncontrolled biases can lead to the uneven distribution of opportunities and resources [58, 78], exacerbating the “ 𝐸𝑐ℎ𝑜 𝐶ℎ𝑎𝑚𝑏𝑒𝑟 ” or “ 𝐹𝑖𝑙𝑡𝑒𝑟 𝐵𝑢𝑏𝑏𝑙𝑒𝑠 ” effects [86, 87].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [58]
The Introduction (CHI 2018 section; page unavailable) says biased resume-search rankings may disadvantage candidates, and the Interpretation section explains that candidates who are not seen have no chance of an interview or hire. This supports uneven access to hiring opportunities, but the paper does not establish uneven distribution of resources across recommender systems generally. The citing sentence attaches separate citations to echo chambers and filter bubbles, so those effects are not attributed to this source. (LLM, conf=0.83)
Experimental — accuracy not yet measuredGoogle, Twitter, and Pinterest all provide similar features, called Customer Match, Tailored Audiences, and Customer Lists, respectively.
“Google, Twitter, and Pinterest all provide similar features; these are called Customer Match[7], Tailored Audiences, and Customer Lists[61], respectively.”
From Discrimination through Optimization (2019) · cites this paper as [7]
The Hiring Websites section (page not provided) discusses resume-search services, not advertising audience features. Searches of our full-text copy for Customer Lists, Tailored Audiences, Customer Match, and Pinterest found no passage addressing the attributed feature names. (LLM, conf=0.76)
Experimental — accuracy not yet measuredAutomated interviews, including the use of speech and facial recognition software, have been shown to perform poorly, particularly with regards to women of colour.
“Automated interviews, including the use of speech and facial recognition software, has been shown to perform poorly, particularly with regards to women of colour [15,16].”
From What does it mean to 'solve' the problem of discrimination in hiring?: social, technical and legal perspectives from the UK on automated hiring systems (2020) · cites this paper as [16]
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Understanding Decision-Making in Recruitment
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- Authors
- Chen, L., Ma, R., Hannák, A., & Wilson, C.
- Affiliations
- Northeastern University (US)
- Rutgers, The State University of New Jersey (US)
- Central European University - Budapest Campus (HU)
- Venue
- Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2018-04-21
- Pages
- 1-14
- OpenAlex
- W2796402109
- Cited by
- 155
- References
- 85
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 21 · 2023: 19 · 2022: 30 · 2021: 29 · 2020: 16 · 2019: 21 · 2018: 2
- FWCI
- 30.43
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- NSF (IIS-1408345, IIS-1553088)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Keywords
- gender discrimination, job search engines, algorithmic fairness, indirect discrimination, candidate ranking, gender inference, Indeed, monsters, recruitment bias
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.
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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