On the Societal Impact of Machine Learning

This research investigates the societal impact of machine learning (ML) systems, highlighting their increasing influence on consequential decisions and recommendations. It identifies the risk of discriminatory effects stemming from ML systems often developed without explicit fairness considerations. The thesis contributes methods for measuring fairness, decomposing systems to anticipate bias, and implementing interventions to mitigate algorithmic discrimination while preserving system utility.

The pervasive integration of machine learning into decision-making processes necessitates a deep understanding of its societal consequences, particularly concerning fairness and potential discrimination. Addressing these issues is critical for maintaining public trust, ensuring equitable outcomes, and fostering the responsible adoption of advanced technologies across industries.

Machine learning systems increasingly inform critical decisions and recommendations across various sectors.

Forward consideration, not a verified fact.