Generating Fair Universal Representations using Adversarial Models

TL;DR

This paper introduces a data-driven adversarial framework for learning fair universal representations that decouple sensitive attributes from data, ensuring fairness and utility across various tasks.

Contribution

It proposes a novel constrained minimax adversarial approach to generate representations that are both fair and useful, clarifying optimal strategies against strong adversaries.

Findings

Effective censorship of multiple sensitive features

Maintains accuracy across multiple downstream tasks

Balances fairness and utility successfully

Abstract

We present a data-driven framework for learning fair universal representations (FUR) that guarantee statistical fairness for any learning task that may not be known a priori. Our framework leverages recent advances in adversarial learning to allow a data holder to learn representations in which a set of sensitive attributes are decoupled from the rest of the dataset. We formulate this as a constrained minimax game between an encoder and an adversary where the constraint ensures a measure of usefulness (utility) of the representation. The resulting problem is that of censoring, i.e., finding a representation that is least informative about the sensitive attributes given a utility constraint. For appropriately chosen adversarial loss functions, our censoring framework precisely clarifies the optimal adversarial strategy against strong information-theoretic adversaries; it also achieves the fairness measure of demographic parity for the resulting constrained representations. We evaluate the performance of our proposed framework on both synthetic and publicly available datasets. For these datasets, we use two tradeoff measures: censoring vs. representation fidelity and fairness vs. utility for downstream tasks, to amply demonstrate that multiple sensitive features can be effectively censored even as the resulting fair representations ensure accuracy for multiple downstream tasks.