Algorithmic fairness
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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It may be difficult to determine which types of fairness are most appropriate for a given context and how to balance them appropriately.
“Additionally, it may be difficult to determine which types of fairness are most appropriate for a given context and how to balance them appropriately [4][5][6].”
From Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies (2023) · cites this paper as [6]
Combinations of individual and group fairness ideals using established definitions have been shown to be mathematically intractable.
“Currently, combinations of these ideals using established deinitions have been shown to be mathematically intractable [27,69,177].”
From Fairness in machine learning: A survey (2024) · cites this paper as [177]
Both omission and immunity overlook relationships with proxy variables.
“Both omission and immunity overlook relationships with proxy variables (as discussed in subsection 2.1, we note [291] as an exception here who omit proxies), which can result in increasing bias and discrimination [177], or indirectly concealing discrimination [88].”
From Fairness in machine learning: A survey (2024) · cites this paper as [177]
Overlooking relationships with proxy variables can increase bias and discrimination.
“Both omission and immunity overlook relationships with proxy variables (as discussed in subsection 2.1, we note [291] as an exception here who omit proxies), which can result in increasing bias and discrimination [177], or indirectly concealing discrimination [88].”
From Fairness in machine learning: A survey (2024) · cites this paper as [177]
We cannot combine both individual and group fairness.
“Sadly, we cannot combine both [69,177].”
From Fairness in machine learning: A survey (2024) · cites this paper as [177]
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- Authors
- Kleinberg, J., Ludwig, J., Mullainathan, S., & Rambachan, A.
- Affiliations
- Cornell University (US)
- University of Chicago (US)
- Harvard University (US)
- 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
- 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
- Social Sciences › Safety Research › 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
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.
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