Richard Zemel

Richard Zemel is a professor at the University of Toronto working on machine learning, with interests in fairness, interpretability, and robustness of neural networks. His research group studies how to build more trustworthy and fair AI systems.

Sources in this wiki

• FLIRT: Feedback Loop In-context Red Teaming

Topics

• AI Safety, Model safety, Fairness

Notes

Co-author on FLIRT; known for foundational work on fairness in machine learning and representation learning.