Linear Classifiers that Encourage Constructive Adaptation

Yatong Chen; Jialu Wang; Yang Liu

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arXiv · 2011.00355

Linear Classifiers that Encourage Constructive Adaptation

Yatong Chen·Jialu Wang·Yang Liu

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Abstract

Machine learning systems are often used in settings where individuals adapt their features to obtain a desired outcome. In such settings, strategic behavior leads to a sharp loss in model performance in deployment. In this work, we aim to address this problem by learning classifiers that encourage decision subjects to change their features in a way that leads to improvement in both predicted \emph{and} true outcome. We frame the dynamics of prediction and adaptation as a two-stage game, and characterize optimal strategies for the model designer and its decision subjects. In benchmarks on simulated and real-world datasets, we find that classifiers trained using our method maintain the accuracy of existing approaches while inducing higher levels of improvement and less manipulation.

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Yatong Chen, Jialu Wang, Yang Liu. 2020-10-31. Linear Classifiers that Encourage Constructive Adaptation. https://arxiv.org/abs/2011.00355

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