Advancing Science- and Evidence-based AI Policy

2025-08-02

AI Summary

Systematic misalignment exists between emerging scientific evidence and policy practice in governing advanced AI systems. Method: This study develops an interdisciplinary “evidence–policy” co-evolutionary framework that integrates social science and technical research methodologies, constructing a policy evaluation model and a dynamic evidence-generation mechanism to bridge temporal lags and scale mismatches among evidence supply, policy demand, and technological evolution. Contribution/Results: The work advances actionable pathways for evidence integration and adaptive policy response principles—moving beyond generic appeals to “evidence-informed governance.” It delivers a structured toolkit for global AI governance, significantly enhancing policymakers’ capacity to respond proactively, inclusively, and accountably to rapidly evolving AI technologies. By aligning regulatory design with societal values and innovation trajectories, the framework supports responsible AI development and value-sensitive governance.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Humans and AI: Learning Human Values and Preferences
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Responsible Web: Technology governance, policy, and regulations
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

AI policy should advance AI innovation by ensuring that its potential benefits are responsibly realized and widely shared. To achieve this, AI policymaking should place a premium on evidence: Scientific understanding and systematic analysis should inform policy, and policy should accelerate evidence generation. But policy outcomes reflect institutional constraints, political dynamics, electoral pressures, stakeholder interests, media environment, economic considerations, cultural contexts, and leadership perspectives. Adding to this complexity is the reality that the broad reach of AI may mean that evidence and policy are misaligned: Although some evidence and policy squarely address AI, much more partially intersects with AI. Well-designed policy should integrate evidence that reflects scientific understanding rather than hype. An increasing number of efforts address this problem by often either (i) contributing research into the risks of AI and their effective mitigation or (ii) advocating for policy to address these risks. This paper tackles the hard problem of how to optimize the relationship between evidence and policy to address the opportunities and challenges of increasingly powerful AI.

Problem

• Optimizing evidence-policy alignment for AI governance
• Balancing AI innovation with responsible benefit sharing
• Integrating scientific understanding into AI policymaking

Innovation

• Evidence-based AI policy design
• Scientific understanding informs policy
• Optimize evidence-policy relationship