85 citations · 236 across the 13 of their papers we have counts for
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stat.ML2018
Taking Advantage of Multitask Learning for Fair Classification
Luca Oneto, Michele Donini, Amon Elders +1
A central goal of algorithmic fairness is to reduce bias in automated decision making. An unavoidable tension exists between accuracy gains obtained by using sensitive information…
stat.ML2018★ 85 cited
Empirical Risk Minimization under Fairness Constraints
Michele Donini, Luca Oneto, Shai Ben-David +2
We address the problem of algorithmic fairness: ensuring that sensitive variables do not unfairly influence the outcome of a classifier. We present an approach based on empirical r…