Let's talk about biases in machine learning! Ethics and Society Newsletter #2

On the Societal Impact of Machine Learning

This PhD thesis examines how machine learning (ML) influences society, noting that ML increasingly shapes consequential decisions and recommendations. It highlights the risk of discriminatory effects when fairness is not explicitly considered in data‑driven systems. The work proposes methods for measuring fairness, decomposing ML systems to anticipate bias, and implementing interventions that reduce discrimination while preserving utility, and it outlines future research directions as ML, including generative AI, becomes more integrated into society.

Ethics and Society Newsletter #4: Bias in Text-to-Image Models

Interventions Against Machine-Assisted Statistical Discrimination

arXiv:2310. 04585v5 Announce Type: replace-cross Abstract: I study statistical discrimination driven by verifiable beliefs, such as those generated by machine learning, rather than by humans.

Ethics and Society Newsletter #6: Building Better AI: The Importance of Data Quality

Statistical and Structural Approaches to Algorithmic Fairness

arXiv:2606. 26200v1 Announce Type: cross Abstract: Modern machine learning systems have outgrown their origins as isolated predictive constructs, evolving into complex socio-technical architectures that actively mediate human opportunity.

Fairness Constraints in High-Dimensional Generalized Linear Models

arXiv:2604. 16610v2 Announce Type: replace-cross Abstract: Machine learning models often inherit biases from historical data, raising critical concerns about fairness and accountability.