37 citations · 62 across the 10 of their papers we have counts for
4 papers · 1 filter
The Pragmatic Frames of Spurious Correlations in Machine Learning: Interpreting How and Why They Matter
Samuel J. Bell, Skyler Wang
Learning correlations from data forms the foundation of today's machine learning (ML) and artificial intelligence research. While contemporary methods enable the automatic discover…
An Effective Theory of Bias Amplification
Arjun Subramonian, Samuel J. Bell, Levent Sagun +1
Machine learning models can capture and amplify biases present in data, leading to disparate test performance across social groups. To better understand, evaluate, and mitigate the…
Reassessing the Validity of Spurious Correlations Benchmarks
Samuel J. Bell, Diane Bouchacourt, Levent Sagun
Neural networks can fail when the data contains spurious correlations. To understand this phenomenon, researchers have proposed numerous spurious correlations benchmarks upon which…
Towards Geographic Inclusion in the Evaluation of Text-to-Image Models
Melissa Hall, Samuel J. Bell, Candace Ross +3
Rapid progress in text-to-image generative models coupled with their deployment for visual content creation has magnified the importance of thoroughly evaluating their performance…