信任AI监管？有辨识力的用户对于建立信任和有效AI监管至关重要
Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation
AI总结：
本研究运用进化博弈论定量分析用户、AI创造者与监管者间的困境，提出政府奖励或用户基于监管有效性决策两种机制，以促进可信AI与用户信任的演化。
摘要：
人们普遍认为，某种形式的监管对于激励AI创造者开发值得信赖的系统以及让用户真正信任这些系统都是必要的。但关于这些监管应采取何种形式以及如何实施，存在诸多争论。该领域的大多数工作都是定性的，无法做出正式的预测。在此，我们提出可以使用进化博弈论来定量建模用户、AI创造者和监管者所面临的困境，并提供对不同监管制度可能产生的影响的见解。我们表明，创建值得信赖的AI和用户信任需要激励监管者进行有效监管。我们证明了两种可以实现这一目标的机制的有效性。第一种是政府能够识别并奖励表现良好的监管者。在这种情况下，如果AI系统对用户的风险不太高，那么一定程度上的可信开发和用户信任就会演化出来。然后我们考虑另一种解决方案，即用户可以将其信任决策取决于监管者的有效性。这会导致有效的监管，并因此带来值得信赖的AI开发和用户信任，前提是实施监管的成本不太高。我们的发现强调了从进化博弈论的角度考虑不同监管制度影响的重要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
