Fairness Without Demographics in Repeated Loss Minimization
This addresses fairness issues for minority groups in applications like speech recognition and text autocomplete, offering a novel solution without demographic data, though it builds on existing DRO methods.
The paper tackles the problem of representation disparity in machine learning models, where minority groups suffer higher loss over time due to empirical risk minimization (ERM), and shows that distributionally robust optimization (DRO) controls minority group risk without needing demographic data, with demonstrated improvements in a real-world text autocomplete task.
Machine learning models (e.g., speech recognizers) are usually trained to minimize average loss, which results in representation disparity---minority groups (e.g., non-native speakers) contribute less to the training objective and thus tend to suffer higher loss. Worse, as model accuracy affects user retention, a minority group can shrink over time. In this paper, we first show that the status quo of empirical risk minimization (ERM) amplifies representation disparity over time, which can even make initially fair models unfair. To mitigate this, we develop an approach based on distributionally robust optimization (DRO), which minimizes the worst case risk over all distributions close to the empirical distribution. We prove that this approach controls the risk of the minority group at each time step, in the spirit of Rawlsian distributive justice, while remaining oblivious to the identity of the groups. We demonstrate that DRO prevents disparity amplification on examples where ERM fails, and show improvements in minority group user satisfaction in a real-world text autocomplete task.