Summary of Trust Ai Regulation? Discerning Users Are Vital to Build Trust and Effective Ai Regulation, by Zainab Alalawi et al.
Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation
by Zainab Alalawi, Paolo Bova, Theodor Cimpeanu, Alessandro Di Stefano, Manh Hong Duong, Elias Fernandez Domingos, Anh Han, Marcus Krellner, Bianca Ogbo, Simon T. Powers, Filippo Zimmaro
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY); Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA); Dynamical Systems (math.DS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is proposed in this paper to model the dilemmas faced by users, AI creators, and regulators through evolutionary game theory, enabling quantitative predictions about the effects of different regulatory regimes. The study demonstrates that trustworthy AI development and user trust require effective regulation, which can be incentivized through mechanisms such as government recognition and rewards for good performance. An alternative solution is also explored, where users condition their trust decisions on regulator effectiveness, leading to effective regulation and trustworthy AI development. The findings highlight the importance of considering regulatory regimes from an evolutionary game theoretic perspective. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Regulators need to be incentivized to develop trustworthy AI systems, and this paper shows how this can be done using a special type of math called evolutionary game theory. The authors use this math to model what happens when different groups (users, AI creators, and regulators) make decisions about trust. They find that if regulators are rewarded for doing a good job, people will start to trust the AI systems more. Another way to achieve trustworthy AI is by letting users decide how much they trust an AI system based on how well it was regulated. |
