Related papers: Fairness-Aware Learning from Corrupted Data

Fairness-Aware Learning from Corrupted Data

URL: http://arxiv.org/abs/2102.06004v1
Date: Thu, 11 Feb 2021 13:48:41 GMT
Title: Fairness-Aware Learning from Corrupted Data
Authors: Nikola Konstantinov, Christoph H. Lampert
Abstract summary: We consider fairness-aware learning under arbitrary data manipulations. We show that the strength of this bias increases for learning problems with underrepresented protected groups in the data. We prove that two natural learning algorithms achieve order-optimal guarantees in terms of both accuracy and fairness under adversarial data manipulations.
Score: 33.52974791836553
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Addressing fairness concerns about machine learning models is a crucial step towards their long-term adoption in real-world automated systems. While many approaches have been developed for training fair models from data, little is known about the effects of data corruption on these methods. In this work we consider fairness-aware learning under arbitrary data manipulations. We show that an adversary can force any learner to return a biased classifier, with or without degrading accuracy, and that the strength of this bias increases for learning problems with underrepresented protected groups in the data. We also provide upper bounds that match these hardness results up to constant factors, by proving that two natural learning algorithms achieve order-optimal guarantees in terms of both accuracy and fairness under adversarial data manipulations.

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