- Algorithmic Fairness, Stanford University
- Uncertain: Modern Topics in Uncertainty Estimation, Penn CIS 7000 (Aaron Roth, Penn, Fall 2022)
- Choices and Consequences in Computing, Cornell INFO 1260 / CS 1340.
- Fairness and Privacy: Perspectives from Law and Probability, Harvard CS 126
- On Individual Risk, Harvard CS 333 and Stat 333
- Societal Concerns in Algorithms and Data Analysis, Weizmann
- Foundations of Fairness in Machine Learning (Jamie Morgenstern, University of Washington, Winter 2022, 2020)
- Fairness in Machine Learning (Moritz Hardt, UC Berkeley, Fall 2017)
- Fairness and Privacy: Perspectives of Law and Probability (Cynthia Dwork and Martha Minow, Harvard, Fall 2019)
- Choices and Consequences in Computing (John Kleinberg and Karen Levy, Cornell, Spring 2021)
- Societal Concerns in Algorithms and Data Analysis (Moni Naor and Guy Rothblum, Weitzmann, Spring 2021)
- Algorithms and Inequality (Rediet Abebe, UC Berkeley, Fall 2021)
- The Algorithmic Foundations of Ethical Machine Learning (Juba Ziani, Georgia Tech, Fall 2021)
- AI and Ethics: Mathematical Foundations and Algorithms (Toniann Pitassi and Richard Zemel, University of Toronto, Fall 2019)