Faculty Candidate Seminar
Defining and Ensuring Algorithmic Fairness in Artificial Intelligence
This event is free and open to the publicAdd to Google Calendar
Abstract: Artificial intelligence is increasingly used to make decisions about people in social domains. Failure to take into account its effects on people’s lives risks grave consequences, including enacting and perpetuating discrimination, and more broadly creating AI systems imbued with values we do not intend or desire. In this talk, I will detail the development over the last few years of increasingly sophisticated approaches to formalize and understand the normative impact of AI, and my own contributions to these approaches. Using examples from binary classification, influence maximization, and hiring markets, I will illustrate through theory and experiments the impact that considerations of fairness have in creating and analyzing algorithms. I will provide algorithms for ensuring group-level fairness in binary classification problems, algorithms for how to more equitably spread information in a social network, and a new approach to defining fairness in hiring markets. This work demonstrates how explicit mathematical modeling of the social impact of decision making can reveal new ways to capture the moral impacts of AI, and emphasizes that further progress in this area will be made by creating AI specifically for the surrounding social context in which it is embedded.
Bio: Ben Fish is a postdoctoral fellow at Mila hosted by Fernando Diaz, which he joined after moving from the Fairness, Accountability, Transparency, and Ethics (FATE) Group at Microsoft Research Montréal, also hosted by Fernando Diaz. His research develops methods for machine learning and other computational systems that incorporate human values and social context. This includes scholarship in fairness and ethics in machine learning and learning over social networks. He received his Ph.D. from the University of Illinois at Chicago as a member of the Mathematical Computer Science group. He was previously a visiting researcher at the University of Melbourne and the University of Utah, and earned a B.A. from Pomona College in Mathematics and Computer Science.