Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation
<|reference_start|>Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation: 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.<|reference_end|>
@article{alalawi2024trust,
    title={Trust AI Regulation? Discerning users are vital to build trust and
  effective AI regulation},
    author={Zainab Alalawi, Paolo Bova, Theodor Cimpeanu, Alessandro Di Stefano,
  Manh Hong Duong, Elias Fernandez Domingos, The Anh Han, Marcus Krellner,
  Bianca Ogbo, Simon T. Powers, and Filippo Zimmaro},
    journal={arXiv preprint arXiv:2403.09510},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09510},
    primaryClass={cs.AI cs.CY cs.GT cs.MA math.DS}
}