AI and Ethics
Overview
In this module, students are introduced to philosophical and statistical conceptions of fairness including parity and calibration, and apply these ideas to algorithms that have disparate impacts on different protected groups, including AI algorithms that predict criminal recidivism. Along the way, students consider the ethical upshot of AI and probability in other domains, including climate change and election forecasting.
Contributors
- Ethics materials by Kathleen Creel, Chris Piech, Jerry Cain, Tori Qiu, and Daniel Webber.
Ethics goals
- Understanding how probability can be used to assess the fairness of AI algorithms.