Incentive Compatibility for AI Alignment in Sociotechnical Systems: Positions and Prospects

2024-02-20

arXiv.org

AI Summary

This paper addresses governance and safety challenges arising from the deep societal embedding of AI by introducing the “Incentive-Compatible Socio-Technical Alignment Problem” (ICSAP), which bridges the gap between technical AI alignment and socio-institutional contexts. Moving beyond dominant purely technical approaches, it is the first to systematically integrate incentive compatibility (IC) from game theory, establishing a unified framework grounded in mechanism design, contract theory, and Bayesian persuasion. The paper formally defines ICSAP, analyzes the applicability boundaries of each IC-based approach, and outlines preliminary implementation strategies. By embedding socio-technical considerations into AI alignment research, this work advances interdisciplinary AI governance, enabling dynamic, context-sensitive, and human-consensus-driven AI systems. It thus extends the methodological scope of AI alignment beyond algorithmic optimization toward institutional and behavioral coherence.

Technology Category

• Multiagent Systems: Mechanism Design
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Learning Human Values and Preferences

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Bridging AI technical and societal alignment gaps
• Exploring incentive compatibility in sociotechnical systems
• Addressing AI governance with game theory principles

Innovation

• Leverage game theory for AI alignment
• Integrate mechanism design for incentive compatibility
• Apply contract theory to sociotechnical systems