Improved Adversarial Learning for Fair Classification

TL;DR

This paper introduces a novel adversarial learning approach for fair classification, formulated as a multi-objective optimization problem, with theoretical guarantees and empirical results showing improved fairness and accuracy.

Contribution

It proposes a gradient descent-ascent algorithm with a modified update step for fair classification, providing theoretical insights and outperforming existing methods.

Findings

Achieves comparable or better accuracy than state-of-the-art algorithms.

Performs better in fairness metrics like statistical rate and false discovery rate.

Validated on Adult and synthetic datasets.

Abstract

Motivated by concerns that machine learning algorithms may introduce significant bias in classification models, developing fair classifiers has become an important problem in machine learning research. One important paradigm towards this has been providing algorithms for adversarially learning fair classifiers (Zhang et al., 2018; Madras et al., 2018). We formulate the adversarial learning problem as a multi-objective optimization problem and find the fair model using gradient descent-ascent algorithm with a modified gradient update step, inspired by the approach of Zhang et al., 2018. We provide theoretical insight and guarantees that formalize the heuristic arguments presented previously towards taking such an approach. We test our approach empirically on the Adult dataset and synthetic datasets and compare against state of the art algorithms (Celis et al., 2018; Zhang et al., 2018; Zafar et al., 2017). The results show that our models and algorithms have comparable or better accuracy than other algorithms while performing better in terms of fairness, as measured using statistical rate or false discovery rate.