Multiaccurate Proxies for Downstream Fairness

• URL: http://arxiv.org/abs/2107.04423v1
• Date: Fri, 9 Jul 2021 13:16:44 GMT
• Title: Multiaccurate Proxies for Downstream Fairness
• Authors: Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, and Saeed Sharifi-Malvajerdi
• Abstract summary: We study the problem of training a model that must obey demographic fairness conditions when the sensitive features are not available at training time. We adopt a fairness pipeline perspective, in which an "upstream" learner that does have access to the sensitive features will learn a proxy model for these features from the other attributes. We show that obeying multiaccuracy constraints with respect to the downstream model class suffices for this purpose.
• Score: 20.36220509798361
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the problem of training a model that must obey demographic fairness conditions when the sensitive features are not available at training time -- in other words, how can we train a model to be fair by race when we don't have data about race? We adopt a fairness pipeline perspective, in which an "upstream" learner that does have access to the sensitive features will learn a proxy model for these features from the other attributes. The goal of the proxy is to allow a general "downstream" learner -- with minimal assumptions on their prediction task -- to be able to use the proxy to train a model that is fair with respect to the true sensitive features. We show that obeying multiaccuracy constraints with respect to the downstream model class suffices for this purpose, and provide sample- and oracle efficient-algorithms and generalization bounds for learning such proxies. In general, multiaccuracy can be much easier to satisfy than classification accuracy, and can be satisfied even when the sensitive features are hard to predict.

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