On the Fairness ROAD: Robust Optimization for Adversarial Debiasing
Vincent Grari Thibault Laugel Tatsunori Hashimoto Sylvain Lamprier Marcin Detyniecki
In the field of algorithmic fairness, significant attention has been put on
group fairness criteria, such as Demographic Parity and Equalized Odds.
Nevertheless, these objectives, measured as global averages, have raised
concerns about persistent local disparities between sensitive groups. In this
work, we address the problem of local fairness, which ensures that the
predictor is unbiased not only in terms of expectations over the whole
population, but also within any subregion of the feature space, unknown at
training time. To enforce this objective, we introduce ROAD, a novel approach
that leverages the Distributionally Robust Optimization (DRO) framework within
a fair adversarial learning objective, where an adversary tries to infer the
sensitive attribute from the predictions. Using an instance-level re-weighting
strategy, ROAD is designed to prioritize inputs that are likely to be locally
unfair, i.e. where the adversary faces the least difficulty in reconstructing
the sensitive attribute. Numerical experiments demonstrate the effectiveness of
our method: it achieves Pareto dominance with respect to local fairness and
accuracy for a given global fairness level across three standard datasets, and
also enhances fairness generalization under distribution shift.