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Robust Optimization for Fairness with Noisy Protected Groups | | Serena Wang
; Wenshuo Guo
; Harikrishna Narasimhan
; Andrew Cotter
; Maya Gupta
; Michael I. Jordan
; | | Date: |
21 Feb 2020 | | Abstract: | Many existing fairness criteria for machine learning involve equalizing or
achieving some metric across extit{protected groups} such as race or gender
groups. However, practitioners trying to audit or enforce such group-based
criteria can easily face the problem of noisy or biased protected group
information. We study this important practical problem in two ways. First, we
study the consequences of na{"i}vely only relying on noisy protected groups:
we provide an upper bound on the fairness violations on the true groups $G$
when the fairness criteria are satisfied on noisy groups $hat{G}$. Second, we
introduce two new approaches using robust optimization that, unlike the
na{"i}ve approach of only relying on $hat{G}$, are guaranteed to satisfy
fairness criteria on the true protected groups $G$ while minimizing a training
objective. We provide theoretical guarantees that one such approach converges
to an optimal feasible solution. Using two case studies, we empirically show
that the robust approaches achieve better true group fairness guarantees than
the na{"i}ve approach. | | Source: | arXiv, 2002.9343 | | Services: | Forum | Review | PDF | Favorites | |
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