Mitigating Bias in Algorithmic Employment Screening: Evaluating Claims and Practices
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Raghavan et al. (2019) reports that no firms currently provide information on the validation of their models.
“We would ideally like to compare our AUC to those of commercial providers, butRaghavan et al. (2019) reports that no firms currently provide information on the validation of their models.”
From Hiring as Exploration (2020) · cites this paper as Raghavan et al. (2019)
Cites
Barocas, S., & Selbst, A. D. (2014) · doi:10.2139/ssrn.2477899 · Cited by 3,048 · see citing works
Self-citationFair prediction with disparate impact: a study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · Big Data · doi:10.1089/big.2016.0047 · Cited by 2,159 · see citing works
Meta-analysis of field experiments shows no change in racial discrimination in hiring over time
Quillian, L., Pager, D., Hexel, O., & Midtbøen, A. H. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1706255114 · Cited by 887 · see citing works
50 Years of Test (Un)fairness: Lessons for Machine Learning
Hutchinson, B., & Mitchell, M. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287600 · Cited by 297 · see citing works
Problem Formulation and Fairness
Passi, S., & Barocas, S. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287567 · Cited by 283 · see citing works
Self-citationToward fairness in AI for people with disabilities SBG@a research roadmap
Guo, A., Kamar, E., Vaughan, J. W., Wallach, H., & Morris, M. R. (2020) · ACM SIGACCESS Accessibility and Computing · doi:10.1145/3386296.3386298 · Cited by 128 · see citing works
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Li, D., Raymond, L., & Bergman, P. (2020) · doi:10.3386/w27736 · Cited by 64 · see citing works
- Authors
- Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K.
- Affiliations
- Massachusetts Institute of Technology (US)
- Cornell University (US)
- Corresponding author
- Raghavan, Manish
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2019
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W2949494970
- Cited by
- 51
- References
- 6
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 7 · 2023: 5 · 2022: 3 · 2021: 6 · 2020: 9 · 2019: 3
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Topics
- Social Sciences › Safety Research › 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
- pre-employment assessment, algorithmic bias, bias mitigation, anti-discrimination law, automated hiring, algorithmic transparency
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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