Classification Under Strategic Self-Selection

Guy Horowitz, Yonatan Sommer, Moran Koren, Nir Rosenfeld
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:18833-18858, 2024.

Abstract

When users stand to gain from certain predictive outcomes, they are prone to act strategically to obtain predictions that are favorable. Most current works consider strategic behavior that manifests as users modifying their features; instead, we study a novel setting in which users decide whether to even participate (or not), this in response to the learned classifier. Considering learning approaches of increasing strategic awareness, we investigate the effects of user self-selection on learning, and the implications of learning on the composition of the self-selected population. Building on this, we propose a differentiable framework for learning under self-selective behavior, which can be optimized effectively. We conclude with experiments on real data and simulated behavior that complement our analysis and demonstrate the utility of our approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-horowitz24a, title = {Classification Under Strategic Self-Selection}, author = {Horowitz, Guy and Sommer, Yonatan and Koren, Moran and Rosenfeld, Nir}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {18833--18858}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/horowitz24a/horowitz24a.pdf}, url = {https://proceedings.mlr.press/v235/horowitz24a.html}, abstract = {When users stand to gain from certain predictive outcomes, they are prone to act strategically to obtain predictions that are favorable. Most current works consider strategic behavior that manifests as users modifying their features; instead, we study a novel setting in which users decide whether to even participate (or not), this in response to the learned classifier. Considering learning approaches of increasing strategic awareness, we investigate the effects of user self-selection on learning, and the implications of learning on the composition of the self-selected population. Building on this, we propose a differentiable framework for learning under self-selective behavior, which can be optimized effectively. We conclude with experiments on real data and simulated behavior that complement our analysis and demonstrate the utility of our approach.} }
Endnote
%0 Conference Paper %T Classification Under Strategic Self-Selection %A Guy Horowitz %A Yonatan Sommer %A Moran Koren %A Nir Rosenfeld %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-horowitz24a %I PMLR %P 18833--18858 %U https://proceedings.mlr.press/v235/horowitz24a.html %V 235 %X When users stand to gain from certain predictive outcomes, they are prone to act strategically to obtain predictions that are favorable. Most current works consider strategic behavior that manifests as users modifying their features; instead, we study a novel setting in which users decide whether to even participate (or not), this in response to the learned classifier. Considering learning approaches of increasing strategic awareness, we investigate the effects of user self-selection on learning, and the implications of learning on the composition of the self-selected population. Building on this, we propose a differentiable framework for learning under self-selective behavior, which can be optimized effectively. We conclude with experiments on real data and simulated behavior that complement our analysis and demonstrate the utility of our approach.
APA
Horowitz, G., Sommer, Y., Koren, M. & Rosenfeld, N.. (2024). Classification Under Strategic Self-Selection. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:18833-18858 Available from https://proceedings.mlr.press/v235/horowitz24a.html.

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