The DOI matches its Crossref record; 85 of the 85 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measuredIn the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cumulated gain-based evaluation of IR techniques
Järvelin, K., & Kekäläinen, J. (2002) · ACM Transactions on Information Systems · doi:10.1145/582415.582418 · Cited by 4,841
Bertrand, M., & Mullainathan, S. (2004) · American Economic Review · doi:10.1257/0002828042002561 · Cited by 4,504
MatchIt : Nonparametric Preprocessing for Parametric Causal Inference
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2011) · Journal of Statistical Software · doi:10.18637/jss.v042.i08 · Cited by 4,385
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007) · Political Analysis · doi:10.1093/pan/mpl013 · Cited by 4,285
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501
Barocas, S., & Selbst, A. D. (2014) · doi:10.2139/ssrn.2477899 · Cited by 3,048
Semantics derived automatically from language corpora contain human-like biases
Caliskan, A., Bryson, J. J., & Narayanan, A. (2017) · Science · doi:10.1126/science.aal4230 · Cited by 2,887
Certifying and Removing Disparate Impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783311 · Cited by 1,827
IR evaluation methods for retrieving highly relevant documents
Järvelin, K., & Kekäläinen, J. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130374 · Cited by 1,460
(2013) · Cited by 1,126
Research works printed but not linked: 11
- Quoctrung Bui. 2014. The Most Common Jobs For The Rich, Middle Class And Poor. NPR. (October 2014). http://n.pr/2iSViU0.
- CEB 2011. Most Common Job Titles Posted Online. CEB. (August 2011). http://bit.ly/2iSYb9h.
- Steven Greenhouse. 2013. Supreme Court Raises Bar to Prove Job Discrimination. New York Times. (June 2013). http://nyti.ms/2ppuG3w.
- Faisal Kamiran and Toon Calders. 2010. Classification with No Discrimination by Preferential Sampling. In Proc. of Machine Learning Conference of Belgium and The Netherlands.
- Pauline T. Kim. 2017. Data-Driven Discrimination at Work. William & Mary Law Review 58 (2017).
- Thomas Lee. 2017. LinkedIn, HiQ spat presents big questions for freedom, innovation. San Francisco Chronicle. (July 2017). http://www.sfchronicle.com/ business/article/LinkedIn-HiQ-spat-presentsbig-questions-for-11274133.php.
- Gabriel Magno, Camila Souza Araujo, Wagner Meira Jr., and Virgílio A. F. Almeida. 2016. Stereotypes in Search Engine Answers: Local or Global? CoRR abs/1609.05413 (2016). · arXiv
- Devah Pager and Hana Shepherd. 2008. The sociology of discrimination: Racial discrimination in employment, housing, credit, and consumer markets. Annual review of sociology 34 (2008), 181.
- Christian Sandvig, Kevin Hamilton, Karrie Karahalios, and Cedric Langbort. 2014. Auditing algorithms: Research methods for detecting discrimination on internet platforms. In Proceedings of "Data and Discrimination: Converting Critical Concerns into Productive Inquiry", a preconference at the 64th Annual Meeting of the International Communication Association.
- Latanya Sweeney. 2013. Discrimination in Online Ad Delivery. (2013). http://ssrn.com/abstract=2208240. · DOI
- 10.5555/1996686.1996731 · DOI
Cited by
Sorted by citations
Items 101–155 of 155
Schwabl, P., Haim, M., & Unkel, J. (2024) · Journal of Computational Social Science · doi:10.1007/s42001-024-00283-6 · Cited by 7
Navigating Automated Hiring: Perceptions, Strategy Use, and Outcomes Among Young Job Seekers
Armstrong, L., & Metaxa, D. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711038 · Cited by 12
Epstein, R., Newland, A., & Tang, L. (2025) · PLoS ONE · doi:10.1371/journal.pone.0322900 · Cited by 6
Williams, D. P. (2021) · Philosophy of engineering and technology · doi:10.1007/978-3-030-70099-7_7 · Cited by 7
Addressing diversity in hiring procedures: a generative adversarial network approach
Marra, T., & Kubiak, E. (2024) · AI and Ethics · doi:10.1007/s43681-024-00445-2 · Cited by 6
Urman, A., Makhortykh, M., & Hannák, A. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732026 · Cited by 6
Self-citationAuthenticity in algorithm-aided decision-making
Karlan, B. (2024) · Synthese · doi:10.1007/s11229-024-04716-7 · Cited by 5
Constrained Off-policy Learning over Heterogeneous Information for Fairness-aware Recommendation
Wang, X., Li, Q., Yu, D., Li, Q., & Xu, G. (2023) · ACM Transactions on Recommender Systems · doi:10.1145/3629172 · Cited by 5
Using Artificial Intelligence in Employment: Problems and Prospects of Legal Regulation
Novikov, D. A. (2024) · Journal of Digital Technologies and Law · doi:10.21202/jdtl.2024.31 · Cited by 6
Users’ Perception of Search-Engine Biases and Satisfaction
Han, B., Shah, C. S., & Saelid, D. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-78818-6_3 · Cited by 4
Parasurama, P., Ghose, A., & Ipeirotis, P. G. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3672484 · Cited by 4
Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm
Gómez-Losada, Á., Cortés, G. A., & Duch‐Brown, N. (2022) · Information · doi:10.3390/info13020044 · Cited by 3
A Framework to Maximize Group Fairness for Workers on Online Labor Platforms
Rabaa, A. E., Elbassuoni, S., Hanna, J., Mouawad, A. E., Olleik, A., & Amer-Yahia, S. (2023) · Data Science and Engineering · doi:10.1007/s41019-023-00213-y · Cited by 3
Are Male Candidates Better than Females? Debiasing BERT Resume Retrieval System
Zhang, S., Li, P., & Cai, Z. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945184 · Cited by 3
Künstliche Intelligenz: Handlungsbedarf im Schweizer Recht
Binder, N. B., Burri, T., Lohmann, M. F., Simmler, M., Thouvenin, F., & Vokinger, K. N. (2021) · Jusletter · doi:10.38023/804420df-f61f-441b-bbad-d14e5db1e210 · Cited by 3
Algorithmic Systems Claim Education and The (Re)Production of Education
Hillman, V. (2023) · Postdigital science and education · doi:10.1007/978-3-031-35411-3_9 · Cited by 3
A Conceptual Framework for Evaluating Fairness in SearchPreprint
Das, A., & Lease, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.09328 · Cited by 3
FairCanary: Rapid Continuous Explainable FairnessPreprint
Ghosh, A., Shanbhag, A., & Wilson, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.07057 · Cited by 3
Self-citationImproving Group Fairness Assessments with Proxies
Harvey, E., Lee, M. S. A., & Singh, J. P. (2024) · ACM Journal on Responsible Computing · doi:10.1145/3677175 · Cited by 2
Tyagi, S., Anuj, Qian, W., Xie, J., & Andrews, R. (2024) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2024.100283 · Cited by 2
Biehl, A. G., Perez, D., Ingle, J., & Ames, M. G. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713176 · Cited by 4
Intersectional Software Engineering as a Field
Takaoka, A. J. W., Cutrupi, C. M., & Jaccheri, L. (2025) · Software · doi:10.3390/software4030018 · Cited by 2
Ahmed, S. A. A. (2022) · CDELT Occasional Papers in the Development of English Education · doi:10.21608/opde.2022.282208 · Cited by 3
Researching and writing differently
Boncori, I. (2022) · Policy Press eBooks · doi:10.2307/j.ctv3405pnh.8 · Cited by 29
Strikovic, E., Blassnig, S., Mitova, E., Urman, A., Esser, F., & de Vreese, C. H. (2024) · New Media & Society · doi:10.1177/14614448241263765 · Cited by 8
Wise Fusion : Group Fairness Enhanced Rank Fusion
Cachel, K., & Rundensteiner, E. A. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679649 · Cited by 2
Trucks Don’t Mean Trump: Diagnosing Human Error in Image Analysis
Zamfirescu-Pereira, J. D., Chen, J., Wen, E., Koenecke, A., Garg, N., & Pierson, E. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533145 · Cited by 3
A Mulching ProposalPreprint
Keyes, O., Hutson, J. A., & Durbin, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.06166 · Cited by 4
Investigating Misinformation in Online Marketplaces: An Audit Study on AmazonPreprint
Hussein, E., & Eldardiry, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.12468 · Cited by 4
Hunt, W. T., & O'Reilly, J. (2022) · doi:10.20919/alnb9606 · Cited by 2
Computing GenderPreprint
Vásárhelyi, O., & Brooke, S. (2022) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/admcs · Cited by 2
Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement
Mousavian, M., Abbasiantaeb, Z., Aliannejadi, M., & Crestani, F. (2025) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3731120.3744620 · Cited by 1
Provocation: Contestability in Large-Scale Interactive NLP Systems
Mitra, T. (2021) · Cited by 1
Isingizwe, J., Eiris, R., & Gheisari, M. (2024) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784485293.008 · Cited by 1
Promoting Gender Fair Resume Screening Using Gender-Weighted Sampling
Tyagi, S., & Anuj, A. (2024) · International Conference on Computing, Machine Learning, and Data Science (CMLDS) · doi:10.1145/3661725.3661786 · Cited by 2
What's in a Query: Polarity-Aware Distribution-Based Fair Ranking
Balagopalan, A., Wang, K., Salaudeen, O., Biega, A. J., & Ghassemi, M. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714660 · Cited by 1
Mitigating Bias in Algorithmic Systems: A Fish-Eye View of Problems and Solutions Across Domains.Preprint
Orphanou, K., Otterbacher, J., Kleanthous, S., Batsuren, K., Giunchiglia, F., Bogina, V., Tal, A. S., Hartman, A., & Kuflik, T. (2021) · arXiv (Cornell University) · Cited by 1
LUCID–GAN: Conditional Generative Models to Locate UnfairnessPreprint
Algaba, A., Mazijn, C., Prunkl, C., Danckaert, J., & Ginis, V. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4289597 · Cited by 1
Algorithmic Gender Bias: Investigating Perceptions of Discrimination in Automated Decision-MakingPreprint
Kim, S., Oh, P., & Lee, J. (2024) · doi:10.31234/osf.io/zpqrt · Cited by 1
Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems
Kroll, J. A. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445937 · Cited by 2
Book Review: Invisible Women: Exposing data bias in a world designed for men
Sosnowska, J. (2020) · Organization Studies · doi:10.1177/0170840619899886
ROBINSON, R. L. (2020) · medRxiv · doi:10.1101/2020.02.22.20026740
Szóbeágyazási vektortérmodellek társadalomtudományi alkalmazása
Kmetty, Z. (2022) · Statisztikai Szemle · doi:10.20311/stat2022.2.hu0105
Measuring the misinformation ecosystem
Jiang, S. (2021) · doi:10.17760/d20412317
Algorithmic Vibe in Information Retrieval
Montazeralghaem, A., Craswell, N., White, R. W., Awadallah, A. H., & Byun, B. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583384
LUCID–GAN: Conditional Generative Models to Locate Unfairness
Algaba, A., Mazijn, C., Prunkl, C., Danckaert, J., & Ginis, V. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44070-0_18
Measuring the harms of personalization through advertising
Ali, M. (2023) · Digital Repository Service (Northeastern University) · doi:10.17760/d20620606
Liu, Y., Lin, A., Peng, S., & Cai, Z. (2026) · Systems · doi:10.3390/systems14030286
Upadhyay, S., Pruss, D., DeVrio, A., Gajos, K. Z., & Karusala, N. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791923
Whose Knowledge Counts? Co-Designing Community-Centered AI Auditing Tools with Educators in Hawai'i
Zhao, D., Cha, H., Ryan, M. J., Wang, A., Baker-Ramos, R., Helekahi-Kaiwi, E.-B., Diego, R., Hester, J. D., & Yang, D. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790958 · Cited by 1
Recognizing Privacy Harm: Progress and Pressing Questions
Zeide, E. (2026) · Lex Electronica · doi:10.7202/1125984ar
Bias in Algorithm‐Assisted Decision‐Making: A Theory‐Based Intervention
Li, A., & Fan, S. (2026) · Journal of Applied Social Psychology · doi:10.1111/jasp.70083
Zakaria, K. S., & Khattak, M. N. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01926-5 · Cited by 1
Perceptions of artificial intelligence bias and gender inclusivity: toward a gendered divergence
Au, A. (2026) · Information Communication & Society · doi:10.1080/1369118x.2026.2737402
Roy, F., Ding, X., Choo, K. K. R., & Zhou, P. (2024) · arXiv · doi:10.48550/arxiv.2409.12651 · Cited by 7
Metadata
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › 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 ›
Are you an author, or do you see a problem with this page? Request a review
