Can Algorithms be Explained Without Compromising Efficiency? The Benefits of Detection and Imitation in Strategic Classification

Abstract

Given the ubiquity of AI-based decisions that affect individuals' lives, providing transparent explanations about algorithms is ethically sound and often legally mandatory. How do individuals strategically adapt following explanations? What are the consequences of adaptation for algorithmic accuracy? We simulate the interplay between explanations shared by an Institution (e.g. a bank) and the dynamics of strategic adaptation by Individuals reacting to such feedback. Resorting to an agent-based approach, our model scrutinizes the role of: i) transparency in explanations, ii) detection capacity and iii) behavior imitation. We find that the risks of transparent explanations are alleviated if effective methods to detect faking behaviors are in place. Furthermore, we observe that social learning and imitation --- as often observed across societies --- is likely to alleviate the impacts of (malicious) adaptation.

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