There is general agreement that some form of regulation is necessary both for AI creators to be incentivised to develop trustworthy systems, and for users to actually trust those systems. But there is much debate about what form these regulations should take and how they should be implemented. Most work in this area has been qualitative, and has not been able to make formal predictions. Here, we propose that evolutionary game theory can be used to quantitatively model the dilemmas faced by users, AI creators, and regulators, and provide insights into the possible effects of different regulatory regimes. We show that creating trustworthy AI and user trust requires regulators to be incentivised to regulate effectively. We demonstrate the effectiveness of two mechanisms that can achieve this. The first is where governments can recognise and reward regulators that do a good job. In that case, if the AI system is not too risky for users then some level of trustworthy development and user trust evolves. We then consider an alternative solution, where users can condition their trust decision on the effectiveness of the regulators. This leads to effective regulation, and consequently the development of trustworthy AI and user trust, provided that the cost of implementing regulations is not too high. Our findings highlight the importance of considering the effect of different regulatory regimes from an evolutionary game theoretic perspective.

翻译：普遍认为，某种形式的监管对于激励AI开发者构建可信系统，以及用户真正信任这些系统都是必要的。然而，关于这些监管应采取何种形式以及如何实施仍存在广泛争议。该领域多数研究为定性分析，难以进行形式化预测。本文提出，演化博弈论可定量建模用户、AI开发者与监管者所面临的困境，并为不同监管体制的潜在影响提供洞见。我们证明，建立可信AI与用户信任要求对监管者进行有效激励。我们展示了两种可实现该目标的机制。第一种机制是政府能够识别并奖励表现优异的监管者。在此情况下，若AI系统对用户风险可控，则可演化出一定程度的可信开发与用户信任。第二种替代方案是允许用户根据监管有效性调整信任决策。只要监管实施成本不过高，该机制将催生有效监管，进而促进可信AI开发与用户信任。我们的研究结果从演化博弈论视角凸显了考察不同监管体制影响的重要性。