Thu, 11-Feb-2021 / 4:30pm / Zoom: https://stanford.zoom.us/meeting/register/tJckfuCurzkvEtKKOBvDCrPv3McapgP6HygJ
Consequential decisions compel individuals to react in response to the specifics of the decision rule. This individual-level response in aggregate can disrupt the statistical patterns that motivated the decision rule, leading to unforeseen consequences.
In this talk, I will discuss two ways to formalize dynamic decision making problems. One, called performative prediction, makes macro-level assumptions about the aggregate population response to a decision rule. The other, called strategic classification, follows microeconomic theory in modeling individuals as utility-maximizing agents with perfect information. We will see key results and limitations of either approach. Drawing on lessons from the microfoundations project in economics, I will outline a viable middleground between the two.
Moritz Hardt is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. Hardt investigates algorithms and machine learning with a focus on reliability, validity, and societal impact. After obtaining a PhD in Computer Science from Princeton University, he held positions at IBM Research Almaden, Google Research and Google Brain. Hardt is a co-founder of the Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) and a co-author of the forthcoming textbook “Fairness and Machine Learning”. He has received an NSF CAREER award, a Sloan fellowship, and best paper awards at ICML 2018 and ICLR 2017.