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.
|Original language||American English|
|Number of pages||9|
|Journal||Proceedings of Machine Learning Research|
|State||Published - 2021|
|Event||24th International Conference on Artificial Intelligence and Statistics, AISTATS 2021 - Virtual, Online, United States|
Duration: 13 Apr 2021 → 15 Apr 2021
Bibliographical noteFunding Information:
Part of this work was done while the authors were visiting the Simons Institute for the Theory of Computing. The work of Yahav Bechavod and Katrina Ligett was supported in part by Israel Science Foundation (ISF) grants #1044/16 and 2861/20, the United States Air Force and DARPA under contracts FA8750-16-C-0022 and FA8750-19-2-0222, and the Federmann Cyber Security Center in conjunction with the Israel national cyber directorate. Yahav Bechavod was also supported in part by the Apple Scholars in AI/ML PhD Fellowship. Katrina Ligett was also funded in part by in part by a grant from Georgetown University and Simons Foundation Collaboration 733792. Zhiwei Steven Wu was supported in part by the NSF FAI Award #1939606, a Google Faculty Research Award, a J.P. Morgan Faculty Award, a Facebook Research Award, and a Mozilla Research Grant. Juba Ziani was supported in part by the Inaugural PIMCO Graduate Fellowship at Caltech, the National Science Foundation through grant CNS-1518941, as well as the Warren Center for Network and Data Sciences at the University of Pennsylvania. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force and DARPA. We thank Mohammad Fereydounian and Aaron Roth for useful discussions.
Copyright © 2021 by the author(s)