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Supervised learning models often make systematic errors on rare subsets of the data.
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Distributionally robust language modeling
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Fairness without demographics in repeated loss minimization
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Fairness without demographics through adversarially reweighted learning
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Distributionally robust losses for latent covariate mixtures
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Exchanging lessons between algorithmic fairness and domain generalization
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Underspecification presents challenges for credibility in modern machine learning
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The MovieLens datasets: History and context
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Character-level convolutional networks for text classification
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SQuAD: 100,000+ questions for machine comprehension of text
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J.; and Gebru, T. 2018 · 2018
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Why is my classifier discriminatory?
Chen, I. Y.; Johansson, F. D.; and Sontag, D. 2018 · 2018
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Identifying medical diagnoses and treatable diseases by image-based deep learning
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Errudite: Scalable, reproducible, and testable error analysis
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Racial disparities in automated speech recognition
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No subclass left behind: Fine-grained robustness in coarse-grained classification problems
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Model cards for model reporting
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Distributionally robust neural networks
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