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.
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“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]
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“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]
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“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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Cites
Cumulated gain-based evaluation of IR techniques
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MatchIt : Nonparametric Preprocessing for Parametric Causal Inference
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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
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(2013) · Cited by 1,126
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Köchling, A., & Wehner, M. C. (2020) · Business Research · doi:10.1007/s40685-020-00134-w · Cited by 511
Roles for computing in social change
Abebe, R., Barocas, S., Kleinberg, J. M., Levy, K. E. C., Raghavan, M., & Robinson, D. G. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3372871 · Cited by 341
Discrimination through Optimization
Ali, M., Sapiezynski, P., Bogen, M., Korolova, A., Mislove, A., & Rieke, A. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359301 · Cited by 268
“s “black boxes” and observe their reactions to di˛erent inputs. Among most notable results, researchers have shown price discrimination in online retail sites [40], gender discrimination in job sites [15, 41], stereotypical gender roles re-enforced by online translation services [12] and image search [43], disparate p…”
BackgroundMeasuring Misinformation in Video Search Platforms: An Audit Study on YouTube
Hussein, E., Juneja, P., & Mitra, T. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3392854 · Cited by 268
Sánchez-Monedero, J., Dencik, L., & Edwards, L. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372849 · Cited by 196
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
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