Roadmap on Incentive Compatibility for AI Alignment and Governance in Sociotechnical Systems
This work addresses the problem of AI misalignment in sociotechnical systems for researchers and policymakers, but it is incremental as it builds on existing game theory concepts without new empirical results.
The paper identifies a gap in AI alignment research, proposing the Incentive Compatibility Sociotechnical Alignment Problem (ICSAP) to integrate game theory principles like mechanism design into sociotechnical systems, aiming to align AI with human societies across contexts.
The burgeoning integration of artificial intelligence (AI) into human society brings forth significant implications for societal governance and safety. While considerable strides have been made in addressing AI alignment challenges, existing methodologies primarily focus on technical facets, often neglecting the intricate sociotechnical nature of AI systems, which can lead to a misalignment between the development and deployment contexts. To this end, we posit a new problem worth exploring: Incentive Compatibility Sociotechnical Alignment Problem (ICSAP). We hope this can call for more researchers to explore how to leverage the principles of Incentive Compatibility (IC) from game theory to bridge the gap between technical and societal components to maintain AI consensus with human societies in different contexts. We further discuss three classical game problems for achieving IC: mechanism design, contract theory, and Bayesian persuasion, in addressing the perspectives, potentials, and challenges of solving ICSAP, and provide preliminary implementation conceptions.