AI Fairness through Robustness

Abstract

The goal of this tutorial is to elucidate the unique and novel connections between algorithmic fairness and the rich literature on adversarial machine learning. Compared to other tutorials on AI fairness, this tutorial will emphasize the connection between recent advances in fair learning and adversarial robustness literature. The range of the presented techniques will cover a complete fairness pipeline, starting from auditing ML models for fairness violations, post-processing them to rapidly alleviate bias, and re-training or fine-tuning models to achieve algorithmic fairness. Each topic will be presented in the context of adversarial ML, specifically, (i) connections between fair similarity metrics for individual fairness and adversarial attack radius, (ii) auditing as an adversarial attack, (iii) fair learning as adversarial training, (iv) distributionally robust optimization for group fairness. We will conclude with (v) a summary of the most recent advances in adversarial ML and its potential applications in algorithmic fairness. The tutorial is designed for a broad range of audiences, including researchers, students, developers, and industrial practitioners. Basic knowledge of machine learning and deep learning is preferred but not required. All topics will be supported with relevant background and demonstrations on varying real data use-cases utilizing Python libraries for fair machine learning.