INAF U6545: Artificial Intelligence: A Survey for Policymakers

This is a mirror of course materials for the course “Artificial Intelligence: A Survey for Policymakers,” to be taught at Columbia University’s School of International and Public Affairs in Spring 2023. Additional information will be posted through the course of the semester.

Course description

Artificial Intelligence (AI) and machine learning have emerged as ubiquitous technologies in a wide range of areas, such as finance, healthcare, consumer internet platforms, and advertising, in addition to several domains in the public sector, including but not limited to law enforcement. In the past several years, ethical questions about how and whether to use AI for particular tasks have become much more prominent, partly due to its widespread use and partly due to publicly documented failures or shortcomings of a number of systems that can negatively impact people in sometimes serious ways.

This course will provide a broad overview of practical and ethical questions related to AI — such as those related to privacy, cybersecurity, fairness, transparency, and more — with a view towards policymaking. Policymaking will be interpreted broadly, including both the public and private sectors. The course will include a survey of how machine learning works so as to ground the discussion.

The instructor recently served as the first Director of AI for New York City and will draw on this experience, which included collaborations with a number of other city governments internationally. The course will discuss and highlight a range of topics in urban policy and urban affairs, using concrete examples and case studies. There will also be opportunities for students to apply the material to areas in the Global South and other areas of interest.

About the instructor

Neal Parikh is a computer scientist who most recently served as the first Director of Artificial Intelligence for New York City. Previously, he co-founded a technology startup, which was acquired after 10 years in operation; was Inaugural Fellow at the Aspen Tech Policy Hub at the Aspen Institute; and worked as a senior quantitative analyst at the Quantitative Investment Strategies Group at Goldman Sachs. He received his Ph.D. in computer science from Stanford University, focusing on large-scale machine learning and convex optimization, and his research has received over 20,000 citations in the literature and is widely used in industry.

Syllabus

  1. Computer science, artificial intelligence, and algorithms
  2. Machine learning
  3. AI in NYC government
  4. Business, trade, and economics
  5. Labor and the workforce
  6. Digital rights and responsible AI
  7. Privacy, security, and accountability
  8. Workforce, finance, and medicine
  9. Criminal justice and risk assessment
  10. Policing, defense, and military applications
  11. Facial recognition and self-driving cars
  12. Regulation, enforcement, and oversight
  13. Investment and R&D
  14. Special topics