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.