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.

Fairness Interventions in Classification: A Study on AI Explainability

arXiv:2407. 14766v4 Announce Type: replace-cross Abstract: This paper presents a philosophical and experimental study of fairness interventions in AI classification, centered on the explainability and transparency of corrective methods, and on the opposition between two fairness criteria, namely Demographic Parity and Equalized Odds.

The Role of Uncertainty in Assessing the Fairness of Machine Learning Models

arXiv:2609.07959v1 Announce Type: cross Abstract: Machine learning models are widely used in clinical applications, social media, law enforcement and critical infrastructure. Verifying whether their...

AI safety needs social scientists

We’ve written a paper arguing that long-term AI safety research needs social scientists to ensure AI alignment algorithms succeed when actual humans are involved. Properly aligning advanced AI systems with human values requires resolving many uncertainties related to the psychology of human rationality, emotion, and biases.

Ethics and Society Newsletter #1

Equality, Equity, and Causality in Fairness Research: A Commentary on Cheng (2026)

arXiv:2607. 17679v1 Announce Type: cross Abstract: This is an invited commentary on the Psychometrika focus article "Fairness Issues and Evaluation in Psychometrics and AI/ML: What Can We Learn from Each Field?

Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation

arXiv:2606. 06514v1 Announce Type: new Abstract: Machine learning systems deployed in high stakes socioeconomic settings routinely display bias.