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Agreement and Alignment for Human-AI Collaboration – Aaron Roth
Solutions to AI: One-Size-Fits-All | Michael Kearns & Aaron Roth
Mathematical Foundations of AI Alignment Lecture 5.1 (Maximal Lotteries and No Regret Learning)
Aaron Roth: Online Multivalid Learning: Game Theory for Better Prediction
Michael Kearns & Aaron Roth, The Ethical Algorithm
ESIF-AIML 2026 | Calibration in the Age of AI by Aaron Roth (University of Pennsylvania)
Understanding Ethics and Algorithms | Michael Kearns & Aaron Roth
Games, Decisions & Networks Seminar by Aaron Roth (UPenn), February 5, 2021
Online Adversarial Multicalibration And (Multi)Calibeating
Mathematical Foundations of AI Alignment, Lecture 7 (Calibration as Decision Alignment)
Multicalibration Lower Bounds And AI Proof Assistance
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Last Updated: October 2, 2026
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toc4fairness.org/toc4fairness-seminar- aaroth.github.io/cis-7000-ai-alignment/ We derive Blackwell's approachability theorem and use it to show how to make ... Gerald M. Masson Distinguished Lecture Series October 23, 2025 “Agreement and Alignment for Human-AI Collaboration” Understanding and improving the science behind the algorithms that run our lives is rapidly becoming one of the most pressing ... Computer and Information Science professors Michael Kearns & ESIF-AIML 2026 | "Calibration in the Age of AI: From Prediction to Decision Making to AI Assisted Research" by We present our recent lower bounds characterizing the optimal obtainable rates for multicalibration, and discuss the workflow that ...
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