Fairness Under Demographic Scarce Regime

TL;DR

This paper introduces a novel framework for improving fairness-accuracy tradeoffs in scenarios with limited demographic data by incorporating uncertainty awareness into attribute classifiers, outperforming traditional methods.

Contribution

The proposed framework enhances fairness-accuracy tradeoffs by enforcing fairness on uncertain sensitive attributes, often surpassing models using true sensitive attributes.

Findings

Framework yields better fairness-accuracy tradeoffs than classic classifiers.

Outperforms models trained with true sensitive attributes in most benchmarks.

Uncertainty measures like conformal prediction support the approach.

Abstract

Most existing works on fairness assume the model has full access to demographic information. However, there exist scenarios where demographic information is partially available because a record was not maintained throughout data collection or for privacy reasons. This setting is known as demographic scarce regime. Prior research has shown that training an attribute classifier to replace the missing sensitive attributes (proxy) can still improve fairness. However, using proxy-sensitive attributes worsens fairness-accuracy tradeoffs compared to true sensitive attributes. To address this limitation, we propose a framework to build attribute classifiers that achieve better fairness-accuracy tradeoffs. Our method introduces uncertainty awareness in the attribute classifier and enforces fairness on samples with demographic information inferred with the lowest uncertainty. We show empirically that enforcing fairness constraints on samples with uncertain sensitive attributes can negatively impact the fairness-accuracy tradeoff. Our experiments on five datasets showed that the proposed framework yields models with significantly better fairness-accuracy tradeoffs than classic attribute classifiers. Surprisingly, our framework can outperform models trained with fairness constraints on the true sensitive attributes in most benchmarks. We also show that these findings are consistent with other uncertainty measures such as conformal prediction.