Abstract
In this paper, we study the prediction of a real-valued target, such as a risk score or recidivism rate, while guaranteeing a quantitative notion of fairness with respect to a protected attribute such as gender or race. We call this class of problems fair regression. We propose general schemes for fair regression under two notions of fairness: (1) statistical parity, which asks that the prediction be statistically independent of the protected attribute, and (2) bounded group loss, which asks that the prediction error restricted to any protected group remain below some pre-determined level. While we only study these two notions of fairness, our schemes are applicable to arbitrary Lipschitzcontinuous losses, and so they encompass least-squares regression, logistic regression, quantile regression, and many other tasks. Our schemes only require access to standard risk minimization algorithms (such as standard classification or least-squares regression) while providing theoretical guarantees on the optimality and fairness of the obtained solutions. In addition to analyzing theoretical properties of our schemes, we empirically demonstrate their ability to uncover fairness-accuracy frontiers on several standard datasets.
Original language | English (US) |
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Title of host publication | 36th International Conference on Machine Learning, ICML 2019 |
Publisher | International Machine Learning Society (IMLS) |
Pages | 166-183 |
Number of pages | 18 |
ISBN (Electronic) | 9781510886988 |
State | Published - 2019 |
Event | 36th International Conference on Machine Learning, ICML 2019 - Long Beach, United States Duration: Jun 9 2019 → Jun 15 2019 |
Publication series
Name | 36th International Conference on Machine Learning, ICML 2019 |
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Volume | 2019-June |
Conference
Conference | 36th International Conference on Machine Learning, ICML 2019 |
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Country/Territory | United States |
City | Long Beach |
Period | 6/9/19 → 6/15/19 |
Bibliographical note
Publisher Copyright:© 36th International Conference on Machine Learning, ICML 2019. All rights reserved.