Core Concepts
Trustworthy AI development requires effective regulation incentivized by user trust.
Abstract
The content discusses the necessity of regulation in AI development to build trust among users. It proposes using evolutionary game theory to model the interactions between users, AI creators, and regulators. The key findings include:
Importance of regulators being incentivized to regulate effectively for trustworthy AI.
Role of conditional trust in breaking cyclic dynamics and promoting stable trust.
Policy implications for overcoming barriers to effective regulation and building user trust.
Suggestions for governments to invest in regulatory capacity and communicate reliable information to users.
The content is structured as follows:
I. Introduction: Debates on AI regulation and the need for trustworthy systems.
II. Models and Methods: Three-population model of AI governance explained.
III. Equilibrium Analysis: Stability analysis for different equilibria scenarios.
IV. Stochastic Analysis: Numerical results for finite population settings.
V. Discussion: Key takeaways, implications for AI governance, limitations, and future research areas.
Overall, the content emphasizes the importance of effective regulation in building trust in AI systems.
Stats
Governments want trustworthy AI systems (EU AI act).
Safety critical systems regulated like automotive vehicles and medical devices.
Evolutionary game theory used to model dilemmas faced by users, creators, regulators.
Quotes
"Most work in this area has been qualitative and does not lead to formal predictions."
"Our findings highlight the importance of considering different regulatory regimes from an evolutionary game theoretic perspective."
"Users can benefit from using an AI system but also run the risk that the system may not act in their best interest."