Algorithmic Fairness

Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan and Ashesh Rambachan

AEA Papers and Proceedings, 2018, vol. 108, 22-27

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.

JEL-codes: C38 D63 I23 J15 (search for similar items in EconPapers)
Date: 2018
Note: DOI: 10.1257/pandp.20181018
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Citations: View citations in EconPapers (21)

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