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2024 ICML ICML 2024

Evaluating Model Bias Requires Characterizing its Mistakes

Abstract

No abstract available. No abstract was captured for this paper when its proceedings were scraped - most often an older edition published without one online. How the corpus is built.

Authors

Isabela Albuquerque, Jessica Schrouff, David Warde-Farley, Ali Taylan Cemgil, Sven Gowal, Olivia Wiles

Topics

Artificial Intelligence > Core AI > Interpretability Machine Learning > Application Areas > Domain Adaptation Deep Learning > Models > Generative Models

Keywords

spurious correlation worst-group accuracy bias accuracy gap mistakes skewsize
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