The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
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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In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“Sadly, we cannot combine both [69,177].”
From Fairness in machine learning: A survey (2024) · cites this paper as [177]
Citations
Cites
The Quarterly Journal of Economics
Kuznets, S. (2024) · Routledge eBooks · doi:10.4324/9781003549741-8 · Cited by 7,177
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
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-citationKleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015) · American Economic Review · doi:10.1257/aer.p20151023 · Cited by 584
Self-citationProceedings of the 3rd Innovations in Theoretical Computer Science Conference
(2012) · doi:10.1145/2090236 · Cited by 140
Fryer, R. G., & Loury, G. C. (2013) · Journal of Political Economy · doi:10.1086/671180 · Cited by 68
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
Cited by
Sorted by citations
Ferrara, E. (2023) · Sci · doi:10.3390/sci6010003 · Cited by 875
“For example, adjusting the model’s predictions to ensure fairness may result in trade-offs between different forms of bias and may have unintended consequences on the distribution of outcomes for different groups [4–6 ,”
BackgroundInfluentialAlgorithmic Fairness: Choices, Assumptions, and Definitions
Mitchell, S., Potash, E., Barocas, S., D'Amour, A., & Lum, K. (2021) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-042720-125902 · Cited by 687
A Framework of Potential Sources of Harm Throughout the Machine Learning Life Cycle
Suresh, H., & Guttag, J. V. (2021) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 576
Predictably Unequal? The Effects of Machine Learning on Credit Markets
FUSTER, A., GOLDSMITH‐PINKHAM, P., RAMADORAI, T., & WALTHER, A. (2022) · The Journal of Finance · doi:10.1111/jofi.13090 · Cited by 872
Fairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646
Metadata
- 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
- 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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