Recent AI algorithms are blackbox models whose decisions are difficult to interpret. eXplainable AI (XAI) seeks to address lack of AI interpretability and trust by explaining to customers their AI decision, e.g., decision to reject a loan application. The common wisdom is that regulating AI by mandating fully transparent XAI leads to greater social welfare. This paper challenges this notion through a game theoretic model for a policy-maker who maximizes social welfare, firms in a duopoly competition that maximize profits, and heterogenous consumers. The results show that XAI regulation may be redundant. In fact, mandating fully transparent XAI may make firms and customers worse off. This reveals a trade-off between maximizing welfare and receiving explainable AI outputs. We also discuss managerial implications for policy-maker and firms.

通过博弈论模型，本文挑战了强制要求完全透明的可解释人工智能(XAI)法规导致更大社会福利的普遍观念，并发现可解释人工智能(XAI)法规可能是多余的，事实上，强制要求完全透明的可解释人工智能(XAI)法规可能会使公司和客户陷入困境。这揭示了在最大化福利和接收可解释的人工智能输出之间存在权衡，同时也讨论了对政策制定者和公司的管理意义。

出售黑盒子！监管可解释人工智能（XAI）可能会损害消费者