Title: Gaming Helps! Learning from Strategic Interactions in Natural Dynamics
Speaker: Katrina Ligett, Associate Professor of Computer Science at the Hebrew University of Jerusalem
Date: October 20
Event link: https://stanford.zoom.us/j/97941677010?pwd=VW9DM1Z1dGZhSnFEcnpvNE9BNGplQT09
Those who are being classified are sometimes aware of the classifiers that are being used on them, and may have incentive to change their behavior to try to improve the label they receive. The attitude towards such strategic behavior, both in practice and in theoretical treatment, has generally been quite negative, and this is one of the reasons that the internal workings of high-stakes classifiers are often shrouded in secrecy.
However, intuitively, agents who strategically change their behavior in response to incentives set by classifiers may actually be doing everyone a favor: they are helping teach the classifier whether the variables that the classifier depends on are truly meaningful for the task at hand---that is, features that, when changed, affect the true label (as opposed to non-meaningful features that have no effect). Over time, this could push the system to develop better classifiers, and could push individuals to invest more in meaningful variables. We study this issue in an online regression setting.
Joint work with Yahav Bechavod, Zhiwei Steven Wu, and Juba Ziani. Work appeared at AISTATS'21.
Katrina Ligett is an Associate Professor of Computer Science at the Hebrew University of Jerusalem, where she is also the Head of the Program on Internet & Society. Her research interests include data privacy, algorithmic fairness, machine learning theory, and algorithmic game theory. She received her PhD in computer science in 2009, from Carnegie Mellon University, and did a postdoc at Cornell University. Before joining the Hebrew University, Katrina was on the faculty in computer science and economics at Caltech.