On the Societal Impact of Machine Learning
It addresses fairness issues in ML systems that affect society broadly, but it is incremental as it builds on existing fairness research.
This PhD thesis tackles the problem of discriminatory effects in machine learning systems by developing methods to measure fairness, anticipate bias, and reduce algorithmic discrimination while maintaining utility, offering a foundation for aligning ML's societal impact with social values.
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.