Issues

Artificial Intelligence

Artificial intelligence (AI) refers to technologies that perceive, learn and reason in ways that simulate human cognitive abilities. TAP scholars consider AI’s effects on labor, business, policing, law, medicine, war, free speech, privacy and democracy, and discuss potential solutions to mitigate harms.

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Upcoming Events

How AI Can Promote Inclusive Prosperity - online event with Frank Pasquale

Hosted by The Allens Hub at UNSW

October 20, 2020,  

Tal Zarsky – When Small Change Makes a Big Difference: Algorithmic Equity Among Similarly Situated Individuals

Presented by the Center for Information Technology Policy

October 20, 2020,  

AI & Future of Work Conference

Presented by the Stanford Digital Economy Lab

October 27, 2020,  

The Growing Use of Artificial Intelligence by Government in Courts and Agencies

Presented by Silicon Flatirons

December 2, 2020,  

TAP Blog

Professors Acemoglu and Athey Share Their Expertise on the Impact of AI on Jobs and the Economy at Today’s Congressional Hearing

Economists Susan Athey, Stanford, and Daron Acemoglu, MIT, will be testifying at today’s House Budget Committee hearing on artificial intelligence and the workforce.

TAP Staff Blogger

Fact Sheets

Artificial Intelligence

Artificial intelligence (AI) refers to technologies that perform learning and reasoning in ways that simulate human cognitive abilities.

Quote

Getting the First Amendment Wrong

Clearview AI is wrong about privacy and wrong about the First Amendment. It would have you believe that the moment you post a photo of yourself on Facebook or walk outside your house, you abandon any privacy interest in your image or your whereabouts because they are now “public.” — Woodrow Hartzog, Professor of Law, Northeastern University and Neil Richards, Professor of Law, Washington University
Woodrow Hartzog
Boston Globe
September 4, 2020

Featured Article

Generalizability: Machine Learning and Humans-in-the-Loop

Machine learned-based software increasingly plays a role in key decision-making systems. The software is “trained” using large datasets; designing effective, fair systems that make accurate predictions when applied to new data is challenging.

By: Katherine Strandburg, John Nay