Fairness in Sociotechnical Machine Learning Systems
Machine learning (ML) algorithms play an increasingly prominent role in the distribution of benefits and burdens in sensitive domains. However, ML systems risk introducing biases that undermine values of justices and fairness, for instance, by perpetuating or even exacerbating unjustifiable harms against vulnerable communities. In response to this concern, a burgeoning field of fair ML has emerged, with researchers developing various fairness measures and methodologies for quantifying and mitigating algorithmic harms. From this perspective, philosophical theorizing about ML tools is often constrained to clarifying the normative underpinnings of fairness measures to help resolve disagreements arising from impossibility results and formal trade-offs. In this talk, I argue that this type of fair ML strategy is usefully characterized as a problematic form of ideal theorizing about justice, and thus suffers from limitations known to plague that approach more broadly. Building on this conceptual framing, I show how the strategy limits our ability to anticipate critical failure modes or propose useful policy remedies. These arguments undermine the ethical grounding of standard auditing and debiasing toolkits, and highlight an urgent need to develop alternative normative frameworks. I provide some suggestions about what these alternative approaches could look like.
Sina Fazelpour is an assistant professor of philosophy and computer science, holding joint appointments with the Khoury College of Computer Sciences and the College of Social Sciences and Humanities at Northeastern University. Fazelpour’s research focuses on issues of justice, diversity, and reliability in data-driven and artificial intelligence technologies. He also works on understanding the concepts and consequences of diversity in social groups and networks. To address these issues, he draws on analytical tools of philosophy, methods of cognitive science, and formal techniques of agent-based simulation and machine learning.
Before joining Northeastern, Fazelpour was an SSHRC Postdoctoral Fellow in the Department of Philosophy at Carnegie Mellon University, with a secondary affiliation with the Machine Learning Department. During 2020-2021, he was the Council Fellow on the World Economic Forum’s Global Future Council on Data Policy. In addition to a doctorate in philosophy, Fazelpour holds a master’s in medical biophysics and a bachelor’s in electrical and biomedical engineering.