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Gaming Helps! Learning from Strategic Interactions in Natural Dynamics
Yahav Bechavod · Katrina Ligett · Steven Wu · Juba Ziani

We consider an online regression setting in which individuals adapt to the regression model: arriving individuals are aware of the current model, and invest strategically in modifying their own features so as to improve the predicted score that the current model assigns to them. Such feature manipulation has been observed in various scenarios---from credit assessment to school admissions---posing a challenge for the learner. Surprisingly, we find that such strategic manipulations may in fact help the learner recover the meaningful variables---that is, the features that, when changed, affect the true label (as opposed to non-meaningful features that have no effect). We show that even simple behavior on the learner's part allows her to simultaneously i) accurately recover the meaningful features, and ii) incentivize agents to invest in these meaningful features, providing incentives for improvement.

Author Information

Yahav Bechavod (Hebrew University)

Yahav Bechavod is a PhD candidate at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, advised by Amit Daniely and Katrina Ligett. He is an Apple PhD fellow in AI/ML, and a recipient of the Charles Clore Foundation PhD Fellowship. He also holds an MS (Computer Science) and a BS (Mathematics and Computer Science), both from the Hebrew University. Yahav's research explores foundational questions in the field of algorithmic fairness, such as: (1) characterizing the amount of friction between utility and fairness in various settings, (2) providing novel algorithms guaranteeing high utility and fairness in the face of limited or partial feedback, and (3) making clever use of human feedback in the learning loop in auditing for unfairness.

Katrina Ligett (Hebrew University)
Steven Wu (Carnegie Mellon University)
Juba Ziani (University of Pennsylvania)

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