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Whenever a binary classifier is used to provide decision support, it typically provides both a label prediction and a confidence value.
Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
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Human representation of visuo-motor uncertainty as mixtures of orthogonal basis distributions
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How model accuracy and explanation fidelity influence user trust
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A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns
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Fair decisions despite imperfect predictions
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Uncalibrated models can improve human-ai collaboration
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Useful confidence measures: Beyond the max score
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Training language models to follow instructions with human feedback
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Distribution-free calibration guarantees for histogram binning without sample splitting
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