Cited by 279Ethics and Social Impacts of AIGame Theory and Voting SystemsNames, Identity, and Discrimination Research

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Algorithmic Fairness

Kleinberg, Jon, Ludwig, Jens, Mullainathan, Sendhil, Rambachan, Ashesh·2018·AEA Papers and Proceedings

DOI 10.1257/pandp.20181018

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Abstract

Concerns that algorithms may discriminate against certain groups have led to numerous efforts to ‘blind’ the algorithm to race. We argue that this intuitive perspective is misleading and may do harm. Our primary result is exceedingly simple, yet often overlooked. A preference for fairness should not change the choice of estimator. Equity preferences can change how the estimated prediction function is used (e.g., different threshold for different groups) but the function itself should not change. We show in an empirical example for college admissions that the inclusion of variables such as race can increase both equity and efficiency.

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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
Fair 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
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
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Human Decisions and Machine Predictions
Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2018) · The Quarterly Journal of Economics · doi:10.1093/qje/qjx032 · Cited by 1,040
Self-citation
Prediction Policy Problems
Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015) · American Economic Review · doi:10.1257/aer.p20151023 · Cited by 584
Self-citation
Proceedings of the 3rd Innovations in Theoretical Computer Science Conference
(2012) · doi:10.1145/2090236 · Cited by 140
Valuing Diversity
Fryer, R. G., & Loury, G. C. (2013) · Journal of Political Economy · doi:10.1086/671180 · Cited by 68

Show all 8 references

Research works printed but not linked: 2

Selbst Andrew D., 2016, California Law Review, 104, 671
Mullainathan Sendhil, 2017, Proceedings of the 8th Conference on Innovation in Theoretical Computer Science, 43, 1

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Authors: Kleinberg, J., Ludwig, J., Mullainathan, S., & Rambachan, A.
Affiliations: Cornell University (US)
University of Chicago (US)
Harvard University (US)
ORCID: Kleinberg, Jon
Ludwig, Jens
Mullainathan, Sendhil
Venue: AEA Papers and Proceedings
Publisher: American Economic Association
Published: 2018-05-01
Volume: 108
Pages: 22-27
ISSN: 2574-0768 (ISSN-L)
2574-0776
DOI: 10.1257/pandp.20181018
OpenAlex: W4248633940
Cited by: 278
References: 8
Citations by year: 2026: 23 · 2025: 35 · 2024: 50 · 2023: 43 · 2022: 44 · 2021: 42 · 2020: 27 · 2019: 10 · 2018: 4
FWCI: 41.26
Type: journal article
Language: en
Open access: closed
Locations: AEA Papers and Proceedings (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
Keywords: algorithmic fairness, algorithmic discrimination, race, equity-efficiency trade-off, university admission, prediction function, thresholding, estimator selection, equity preferences

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