ICML 2020

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Poster

Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing

Sanghamitra Dutta · Dennis Wei · Hazar Yueksel · Pin-Yu Chen · Sijia Liu · Kush Varshney

Thu Jul 16 07:00 AM -- 07:45 AM & Thu Jul 16 06:00 PM -- 06:45 PM (PDT) @ Virtual #None

in Poster Session 42 »

A trade-off between accuracy and fairness is almost taken as a given in the existing literature on fairness in machine learning. Yet, it is not preordained that accuracy should decrease with increased fairness. Novel to this work, we examine fair classification through the lens of mismatched hypothesis testing: trying to find a classifier that distinguishes between two ideal distributions when given two mismatched distributions that are biased. Using Chernoff information, a tool in information theory, we theoretically demonstrate that, contrary to popular belief, there always exist ideal distributions such that optimal fairness and accuracy (with respect to the ideal distributions) are achieved simultaneously: there is no trade-off. Moreover, the same classifier yields the lack of a trade-off with respect to ideal distributions while yielding a trade-off when accuracy is measured with respect to the given (possibly biased) dataset. To complement our main result, we formulate an optimization to find ideal distributions and derive fundamental limits to explain why a trade-off exists on the given biased dataset. We also derive conditions under which active data collection can alleviate the fairness-accuracy trade-off in the real world. Our results lead us to contend that it is problematic to measure accuracy with respect to data that reflects bias, and instead, we should be considering accuracy with respect to ideal, unbiased data.

Keywords: Information Theory and Estimation · Fairness, Equity and Justice · Fairness, Equity, Justice, and Safety

[ Slides]

Author Information

Sanghamitra Dutta (Carnegie Mellon University)

Dennis Wei (IBM Research)

Hazar Yueksel (IBM Research)

Pin-Yu Chen (IBM Research AI)

Sijia Liu (MIT-IBM Watson AI Lab)

Sijia Liu is a Research Staff Member at MIT-IBM Watson AI Lab, IBM research. Prior to joining in IBM Research, he was a Postdoctoral Research Fellow at the University of Michigan, Ann Arbor. He received the Ph.D. degree (with All University Doctoral Prize) in electrical and computer engineering from Syracuse University, NY, USA, in 2016. His recent research interests include deep learning, adversarial machine learning, gradient-free optimization, nonconvex optimization, and graph data analytics. He received the Best Student Paper Finalist Award at Asilomar Conference on Signals, Systems, and Computers (Asilomar'13). He received the Best Student Paper Award at the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'17). He served as a general chair of the Symposium 'Signal Processing for Adversarial Machine Learning' at GlobalSIP, 2018. He is also the co-chair of the workshop 'Adversarial Learning Methods for Machine Learning and Data Mining' at KDD, 2019.