On the Societal Impact of Machine Learning

TL;DR

This thesis explores the societal effects of machine learning, focusing on fairness, bias, and interventions to reduce discrimination, aiming to align ML systems with social values amid increasing societal integration.

Contribution

It introduces methods for measuring fairness, decomposing ML systems to understand bias, and implementing interventions to mitigate discrimination while preserving utility.

Findings

New fairness measurement techniques

Systematic bias decomposition methods

Effective intervention strategies for reducing discrimination

Abstract

This PhD thesis investigates the societal impact of machine learning (ML). ML increasingly informs consequential decisions and recommendations, significantly affecting many aspects of our lives. As these data-driven systems are often developed without explicit fairness considerations, they carry the risk of discriminatory effects. The contributions in this thesis enable more appropriate measurement of fairness in ML systems, systematic decomposition of ML systems to anticipate bias dynamics, and effective interventions that reduce algorithmic discrimination while maintaining system utility. I conclude by discussing ongoing challenges and future research directions as ML systems, including generative artificial intelligence, become increasingly integrated into society. This work offers a foundation for ensuring that ML's societal impact aligns with broader social values.