Fairness Without Demographics in Repeated Loss Minimization

TL;DR

This paper addresses fairness in machine learning without using demographic data by proposing a distributionally robust optimization method that prevents disparity amplification over time, improving minority group outcomes.

Contribution

It introduces a DRO-based approach that controls minority risk without requiring demographic information, preventing fairness degradation over time.

Findings

DRO prevents disparity amplification where ERM fails

Improves minority group user satisfaction in text autocomplete

Controls risk of minority groups over time

Abstract

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.