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Title: Fairness in matching under uncertainty
The prevalence and importance of algorithmic two-sided marketplaces has drawn attention to the issue of fairness in such settings. Algorithmic decisions are used in assigning students to schools, users to advertisers, and applicants to job interviews. These decisions should heed the preferences of individuals, and simultaneously be fair with respect to their merits (synonymous with fit, future performance, or need). Merits conditioned on observable features are always uncertain, a fact that is exacerbated by the widespread use of machine learning algorithms to infer merit from the observables. As our key contribution, we carefully axiomatize a notion of individual fairness in the two-sided marketplace setting which respects the uncertainty in the merits; indeed, it simultaneously recognizes uncertainty as the primary potential cause of unfairness and an approach to address it. We design a linear programming framework to find fair utility-maximizing distributions over allocations, and we show that the linear program is robust to perturbations in the estimated parameters of the uncertain merit distributions, a key property in combining the approach with machine learning techniques.  more » « less
Award ID(s):
2344925 1943584
NSF-PAR ID:
10494446
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
JMLR.org
Date Published:
Journal Name:
Proceedings of the 40th International Conference on Machine Learning
Volume:
202
Page Range / eLocation ID:
7775–7794
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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