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Artificial Intelligence for Policymaking

Key Information

Course details
Option course for MSc, Hilary Term
Assessment
Coursework submission
Reading list
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Tutors
Dr Keegan McBride, Professor Ralph Schroeder

About

This course is not – for the most part – about how to regulate AI. Instead, it examines how AI can be used to improve policymaking and governance. The course will explore how AI can be used to develop, evaluate, and implement better policies; as well as the technical and organizational conditions that enable AI-driven policymaking.

Policymaking relies on evidence-based knowledge. Fuelled by the growing availability of digital data about social phenomena, AI systems can help policymakers evaluate the feasibility and effectiveness of different policy options. However, the practical and theoretical challenges are considerable: AI is often used for prediction and data-driven forecasting, but how reliable is the knowledge produced by AI? What boundary conditions need to be set for different models and data analysis techniques? What units of analysis are relevant? What are the data sources involved? And how can AI models ‘learn to learn’ so as to account for new information as well as an ever-changing policy landscape?

The course will illustrate these themes through different weekly topics with concrete applications of modelling and predictions in each session. The course will focus on government and public policy, though we will also discuss wars and conflict, occupational change, populations and resources, climate change, economic openness, and stability and crisis. (The course does not require any programming or special statistical skills.)

Learning Objectives

At the end of the course, students will, 

  • Have a solid grasp of AI models and how they can be used in policymaking
  • Be able to assess question pertaining to the reliability and limits of AI-based policymaking
  • Understand how knowledge derived from AI relates a number of specific policy domains.

Weekly topics:

  1. Governing with AI
  2. Government Data Infrastructures for AI
  3. The Sociology and Epistemology of AI Knowledge
  4. Risks and Responsible AI Use
  5. AI, Bureaucracy, Rationalization
  6. Emerging Regulatory Frameworks
  7. Learning from the Past, Forecasting the Future
  8. From Knowledge to Practice