2021 Fall Conference on Policy & AI: Four Radical Proposals for a Better Society
November 9, 2021
9:00a.m. - 9:15a.m. PST
Welcome and Introduction
Speakers
William Benjamin Scott and Luna M. Scott Professor of Law; Professor of Political Science; Senior Fellow, SIEPR; Associate Director, HAI; Faculty Fellow, CASBS; Faculty Director, Stanford RegLab
Jerry Yang and Akiko Yamazaki Professor; Senior Fellow, Stanford Institute for Human-Centered AI (HAI); Director of the Stanford Digital Economy Lab; Ralph Landau Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR)
Virtual
9:15a.m. - 9:45a.m. PST
Keynote Talk
Speakers
Eric Lander
President’s Science Advisor and Director of the White House Office of Science and Technology Policy (OSTP)
Virtual
9:45a.m. - 11:00a.m. PST
Middleware Could Give Consumers Choices Over What They See Online
Speakers
Olivier Nomellini Senior Fellow, FSI; Mosbacher Director of the Stanford Center on Democracy, Development, and the Rule of Law
Professor of Management Science and Engineering, and, by courtesy, of Computer Science, Stanford University
Associate Professor of Human Centered Design & Engineering, University of Washington
Associate Professor of Computer Science; Head of Program on Internet & Society; Member of Federmann Center for the Study of Rationality, Hebrew University
Moderators
Research Manager, Stanford Internet Observatory, Stanford Cyber Policy Center, FSI
Virtual
11:00a.m. - 12:15p.m. PST
Universal Basic Income to Offset Job Losses Due to Automation
Speakers
Founder and CEO, Venture for America; Former 2020 Presidential Candidate
Henry Cohen Professor of Economics and Urban Policy; Director of the Institute for the Study of Race, Stratification and Political Economy, The New School Milano
Trione Director and Senior Fellow, Stanford Institute for Economic Policy Research (SIEPR); Wayne and Jodi Cooperman Professor of Economics, Stanford University
Moderators
Assistant Professor of Philosophy, Stanford University
Virtual
12:15p.m. - 12:30p.m. PST
Closing Remarks
Speakers
Virtual
November 10, 2021
9:00a.m. - 9:10a.m. PST
Welcome and Introduction
Speakers
Virtual
9:10a.m. - 9:45a.m. PST
Keynote Talk
Speakers
Director of META (ML Ethics, Transparency, and Accountability), Twitter; Former CEO and Founder, Parity; Former Global Lead of Responsible AI, Accenture Applied Intelligence
Virtual
9:45a.m. - 11:00a.m. PST
Data Cooperatives Could Give Us More Power Over Our Data
Speakers
Associate Political Economist and Social Technologist, Microsoft
Richard M. Sherman, Distinguished Professor of Law, UC Berkeley Law; Professor, UC Berkeley School of Information; Co-Director, Berkeley Center for Law & Technology
Toshiba Professor of Media Arts and Sciences; Professor of Information Technology; Media Lab Entrepreneurship Program Director, MIT Management Sloan School
Moderators
Privacy and Data Policy Fellow, Stanford Institute for Human-Centered Artificial Intelligence (HAI)
Virtual
11:00a.m. - 12:15p.m. PST
Third-Party Auditor Access for AI Accountability
Speakers
Fellow, Mozilla Foundation; Fellow, Algorithmic Justice League; PhD Student in Computer Science, University of California at Berkeley
Head of Technology, Devoted Health; Former Chief Data Scientist, United States Office of Science and Technology Policy
Founder, Mathbabe.org; New York Times Best Selling Author of Weapons of Math Destruction; Former Director of Lede Program in Data Practices, Columbia University
Moderators
Theodore Nierenberg Professor of Economics, Yale University School of Management
Virtual
12:15p.m. - 12:30p.m. PST
Closing Remarks
Speakers
Virtual
Related Events
Ashesh Rambachan | From Next-Token Prediction to Automatic Induction of Automata
Sequence data is ubiquitous in economics — job histories in labor economics, diagnosis and treatment sequences in health economics, strategic interactions in game theory. Generative sequence models can learn to predict these sequences well, but their complexity makes it hard to extract interpretable economic insights from their predictions.
.png&w=1920&q=100)
Sequence data is ubiquitous in economics — job histories in labor economics, diagnosis and treatment sequences in health economics, strategic interactions in game theory. Generative sequence models can learn to predict these sequences well, but their complexity makes it hard to extract interpretable economic insights from their predictions.
Caroline Meinhardt, Thomas Mullaney, Juan N. Pava, and Diyi Yang | How Can AI Support Language Digitization and Digital Inclusion?
What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.
What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.
Matt Beane | Precision Proactivity: Measuring Cognitive Load in Real-World AI-Assisted Work
Systems like ChatGPT and Claude assist billions through proactive dialogue—offering unsolicited, task-relevant information. Drawing on Cognitive Load Theory, we study how cognitive load shapes performance in AI assisted knowledge work.
.png&w=1920&q=100)
Systems like ChatGPT and Claude assist billions through proactive dialogue—offering unsolicited, task-relevant information. Drawing on Cognitive Load Theory, we study how cognitive load shapes performance in AI assisted knowledge work.