Using AI Tools for Quantitative Policy Analysis

(1.5 Credits)

This course teaches students to use and critically assess existing AI tools to support quantitative policy analysis; it does not teach AI model development or the technical methods underlying these tools. Through hands-on exercises using real policy datasets and documents, students structure policy problems, research and substantiate their components, and evaluate potential solutions. They combine spreadsheet-based analytics and traditional policy analysis techniques with AI-assisted text analysis, evidence synthesis, and forecasting. Students treat AI outputs as unverified claims, actively checking for hallucinations, omissions, and systematic bias. They compare AI-assisted work with manual analysis and simple benchmarks, test whether forecasting accuracy improves, and assess how errors could affect policy decisions. Students learn when to validate or supplement outputs, restrict AI use, or avoid it altogether, particularly in high-stakes settings. Transparency, interpretability, governance, and accountable decision-making are integrated throughout. No programming or formal statistical modeling is required.