Linear Classifiers that Encourage Constructive Adaptation

• URL: http://arxiv.org/abs/2011.00355v3
• Date: Thu, 10 Jun 2021 04:13:23 GMT
• Title: Linear Classifiers that Encourage Constructive Adaptation
• Authors: Yatong Chen, Jialu Wang, Yang Liu
• Abstract summary: We study 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.
• Score: 6.324366770332667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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