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

Sell Me the Blackbox! Regulating eXplainable Artificial Intelligence (XAI) May Harm Consumers

We develop a game theoretic model of malware protection using the
state-of-the-art sandbox method, to characterize and compute optimal defense
strategies for anti-malware. We model the strategic interaction between
developers of malware (M) and anti-malware (AM) as a two player game, where AM
commits to a strategy of generating sandbox environments, and M responds by
choosing to either attack or hide malicious activity based on the environment
it senses. We characterize the condition for AM to protect all its machines,
and identify conditions under which an optimal AM strategy can be computed
efficiently. For other cases, we provide a quadratically constrained quadratic
program (QCQP)-based optimization framework to compute the optimal AM strategy.
In addition, we identify a natural and easy to compute strategy for AM, which
as we show empirically, achieves AM utility that is close to the optimal AM
utility, in equilibrium.

开发了一个博弈论模型，使用最先进的沙箱方法进行恶意软件防护，并计算出了最优的防御策略。该模型考虑了恶意软件开发者和防恶意软件之间的策略互动，以及防恶意软件生成沙箱环境的策略和恶意软件依据环境感测本身的攻击或隐藏恶意活动的策略。该模型能够保护所有的机器并计算出最优策略，其中，使用了一个基于 QCQP 的优化框架来处理一些特殊情况，并且发现了一个自然易于计算的方法可以接近最优效用平衡。