Demographic Parity Tails for Regression
This work addresses fairness concerns in regression for applications where full distribution constraints are unnecessary, offering a more targeted approach.
The paper tackles the problem of demographic parity in regression by proposing a framework that enforces fairness constraints only on the tails of the target distribution across sensitive groups, using optimal transport theory, and shows improved flexibility and interpretability with theoretical guarantees and experimental validation.
Demographic parity (DP) is a widely studied fairness criterion in regression, enforcing independence between the predictions and sensitive attributes. However, constraining the entire distribution can degrade predictive accuracy and may be unnecessary for many applications, where fairness concerns are localized to specific regions of the distribution. To overcome this issue, we propose a new framework for regression under DP that focuses on the tails of target distribution across sensitive groups. Our methodology builds on optimal transport theory. By enforcing fairness constraints only over targeted regions of the distribution, our approach enables more nuanced and context-sensitive interventions. Leveraging recent advances, we develop an interpretable and flexible algorithm that leverages the geometric structure of optimal transport. We provide theoretical guarantees, including risk bounds and fairness properties, and validate the method through experiments in regression settings.