Advancing Science- and Evidence-based AI Policy

TL;DR

This paper discusses how AI policy can be improved by better integrating scientific evidence and systematic analysis to responsibly foster AI innovation while managing risks and societal impacts.

Contribution

It offers a framework for aligning evidence-based scientific understanding with policy-making processes in the context of AI governance.

Findings

Evidence-informed policies better address AI risks and opportunities.

Current policies often misalign with scientific evidence due to institutional and political factors.

A systematic approach can enhance the effectiveness of AI regulation.

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.