Regulating Explainable Artificial Intelligence (XAI) May Harm Consumers

Behnam Mohammadi (), Nikhil Malik (), Tim Derdenger () and Kannan Srinivasan ()
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Behnam Mohammadi: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Nikhil Malik: Marshall School of Business, University of Southern California, Los Angeles, California 90089
Tim Derdenger: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Kannan Srinivasan: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

Marketing Science, 2025, vol. 44, issue 3, 711-724

Abstract: The most recent artificial intelligence (AI) algorithms lack interpretability. Explainable artificial intelligence (XAI) aims to address this by explaining AI decisions to customers. Although it is commonly believed that the requirement of fully transparent XAI enhances consumer surplus, our paper challenges this view. We present a game-theoretic model where a policymaker maximizes consumer surplus in a duopoly market with heterogeneous customer preferences. Our model integrates AI accuracy, explanation depth, and method. We find that partial explanations can be an equilibrium in an unregulated setting. Furthermore, we identify scenarios where customers’ and firms’ desires for full explanation are misaligned. In these cases, regulating full explanations may not be socially optimal and could worsen the outcomes for firms and consumers. Flexible XAI policies outperform both full transparency and unregulated extremes.

Keywords: machine learning; explainable AI; economics of AI; regulation; fairness (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:44:y:2025:i:3:p:711-724

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