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Counterfactual examples for an input -- perturbations that change specific features but not others -- have been shown to be useful for evaluating bias of machine learning models, e.g., against specific demographic groups.
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Generalized adversarially learned inference
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Semi-supervised image attribute editing using generative adversarial networks
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Gender slopes: Counterfactual fairness for computer vision models by attribute manipulation
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Deep structural causal models for tractable counterfactual inference
N. Pawlowski, D. C. Castro, and B. Glocker · 2020
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