Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation

TL;DR

This work proposes 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.

Abstract

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.

Figures (8)

• Figure 1: Core features. The figure schematically illustrates the core features of the three-population model of AI governance. Users can either trust (T) or not trust (N) the AI system, in which case they do not adopt the system and get zero benefit. Creators can either defect by creating unsafe AI products (D) or cooperate by creating safe ones (C), which entails additional costs. Applying regulations (C) also comes with some costs, while punishing defecting creators requires further costs.
• Figure 2: Reward for regulators. The figure shows the changes to the original model. Regulators gain extra reward for capturing unsafe development.
• Figure 3: Conditional trust. The figure shows how the state of regulation changes the behaviour of users, which impacts the whole system.
• Figure 4: Numerical integration of the evolution equation for the extended model\ref{['eq: replicator dynamics extended model']}, describing the evolution of the density of trusting users $x(t)$, cooperating creators $y(t)$ and cooperating regulators $z(t)$. In all these simulations: $b_U=4$, $c_P=0.5$, $u=1.5$, $c_R=0.5$, $b_{fo} - v=1.5$. For the initial conditions and the other parameters, see the captions.
• Figure 5: Low regulation cost ($c_R = 0.5$). Conditional trust can lead to full trust, cooperative regulation and safe development. Parameters set to: $b_U=b_R=b_P = 4$, $u=1.5$, $v=0.5$, $c_P=0.5$, $\beta=0.1$, $N_U=N_P=N_R=100$.