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Linear Classifiers that Encourage Constructive Adaptation
Yatong Chen · Jialu Wang · Yang Liu

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 tackle this problem by learning classifiers that encourage decision subjects to change their features in a way that leads to improvement in both predicted and true outcomes---in other words, the classifier provides recourse to decision subjects as long as the adaptation is constructive. We do this by framing 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 our method maintains the accuracy of existing approaches while inducing higher levels of improvement and less manipulation.

Author Information

Yatong Chen (UC Santa Cruz)
Jialu Wang (University of California, Santa Cruz)
Yang Liu (UC Santa Cruz)

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