Fair Representation Learning through Implicit Path Alignment

TL;DR

This paper introduces a novel fair representation learning method using bi-level optimization and implicit path alignment to ensure invariant predictors across sub-groups, improving fairness and performance.

Contribution

It proposes an implicit path alignment algorithm for bi-level fair representation learning, reducing computational costs and fulfilling the sufficiency rule.

Findings

Improved fairness-performance trade-off in classification tasks

Effective invariant predictor learning across sub-groups

Validated on both classification and regression benchmarks

Abstract

We consider a fair representation learning perspective, where optimal predictors, on top of the data representation, are ensured to be invariant with respect to different sub-groups. Specifically, we formulate this intuition as a bi-level optimization, where the representation is learned in the outer-loop, and invariant optimal group predictors are updated in the inner-loop. Moreover, the proposed bi-level objective is demonstrated to fulfill the sufficiency rule, which is desirable in various practical scenarios but was not commonly studied in the fair learning. Besides, to avoid the high computational and memory cost of differentiating in the inner-loop of bi-level objective, we propose an implicit path alignment algorithm, which only relies on the solution of inner optimization and the implicit differentiation rather than the exact optimization path. We further analyze the error gap of the implicit approach and empirically validate the proposed method in both classification and regression settings. Experimental results show the consistently better trade-off in prediction performance and fairness measurement.