Princeton Center for Information Technology Policy

Check out the Princeton Alumni Weekly profile of Andrés Monroy-Hernández, who leads the Human-Computer Interaction Lab at Princeton University. He's working to build tech that serves the greater good — including research aimed at making platforms like Uber, Lyft and DoorDash better and more equitable for gig workers. https://lnkd.in/eQHvqdjJ

Princeton’s high-intensity AI initiatives are pushing the boundaries of multiple fields at once, as University scholars work to accelerate discovery and make AI more accurate, efficient and ethical.

Princeton CITP is launching a new program, AI Policy Precepts, designed to help Federal policymakers explore the core concepts, opportunities, and risks underlying the technology that will shape or influence policy making for the next ten years. Nominations (including self-nominations) for the first cohort are due March 29, 2024. Please share widely! Here is a longer description and a link to the nomination form: https://lnkd.in/ezRRd_cH

New article published! Governments increasingly rely on AI and big data to develop, evaluate, and refine policies. But what are the affordances and limitations of AI-driven policy optimization? In a new article published in Social Science Computer Review, former CITP fellow Jakob Mökander and Oxford Internet Institute Professor Ralph Schroeder argue that the use of AI and big data for policymaking purposes is best understood as an acceleration and intensification of long-standing rationalization and bureaucratization processes. Drawing on Weber, the authors develop their argument through a case study of AI-driven tax policy optimization, and highlight how much of the public outcry and scholarly criticisms directed towards “AI” mirrors well-known critiques of rationalization. By situating contemporary discussions about AI governance in a historic context, the article presents a rich vocabulary for articulating, diagnosing and – potentially – resolving specific problems related to the use of AI in the public sector. https://lnkd.in/eynRimsQ

Honored to be selected as a Sloan Research Fellow. Deeply grateful to my incredible collaborators and students who made the research possible, and to family, mentors and colleagues at Princeton and USC for their unwavering support. https://lnkd.in/epWT4Rg4

U.S. Department of Justice designates Princeton's Jonathan Mayer to serve as the first chief science and technology adviser and chief AI officer. Mayer, an assistant professor of computer science and public affairs and a member of Princeton’s Great Class of 2009, has committed to serve in this position for at least 12 months.

Will AI transform law? In a new paper, Sayash Kapoor, Peter Henderson, and I argue that the hype is not supported by current evidence. Our key thesis is that the areas that would be most transformative if AI were successful are also harder for AI as well as more prone to overoptimism due to evaluation pitfalls. https://lnkd.in/gnbSwBRa

While certain AI technologies are progressing at a rapid rate, many AI tools are not up to speed with the hype. In a new Q&A: https://bit.ly/3SviuKq, Princeton University professor Arvind Narayanan sheds light on the capabilities and pitfalls of artificial intelligence. Read more Discovery: Research at Princeton magazine for free online: https://bit.ly/3SviuKq

Two Princetonians, JAMA editor-in-chief Kirsten Bibbins-Domingo '87 and Princeton Center for Information Technology Policy director Arvind Narayanan, discuss biases in machine learning, data privacy, and the dangers of overestimating AI’s abilities.

Two Princetonians, JAMA editor-in-chief Kirsten Bibbins-Domingo '87 and Princeton Center for Information Technology Policy director Arvind Narayanan, discuss biases in machine learning, data privacy, and the dangers of overestimating AI’s abilities.