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AI for Science Policy

AI for science policy covers how AI tools are being used in policy-relevant research, policy evaluation, and the interface between academic research and policy decisions.

Context & Background

AI creates new opportunities for policy-relevant research by:

  • Scaling policy evaluation: Analyzing more policies across more jurisdictions
  • Real-time analysis: Processing data faster for timely policy input
  • Text analysis of policy documents: Mining legislative text, regulatory filings, and public comments
  • Simulation: Using AI to model policy scenarios and counterfactuals

Practical Implications

  • Focus on actionable insights: Policy audiences need clear, timely results
  • Be transparent about AI use: Policy relevance requires credibility, which requires transparency
  • Validate rigorously: Policy decisions have real-world consequences — verify AI-assisted findings thoroughly

Key Sources