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History
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A Multi-Group Perspective on Fairness
Guy Rothblum and Omer Reingold - A Multi-Group Approach to Algorithmic Fairness - IPAM at UCLA
Multi-group learning via Outcome Indistinguishability - Gal Yona
MLOps Salon: Applying MLOps at Scale - Algorithmic Fairness: From Theory to Practice
LTI Colloquium: Preference Based Evaluation
The Limits of Group Fairness and Predictive Multiplicity
Multi-Distribution Learning, for Robustness, Fairness, and Collaboration
DataLearning: Tackling Fairness, Change, and Polysemy in Word Embeddings
Talk Polymath Ep. 8 | The Social Impact of Algorithmic Fairness
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Last Updated: August 18, 2026
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