Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals. (arXiv:2009.08270v4 [cs.CV] UPDATED)

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. However,
generating counterfactual examples for images is non-trivial due to the
underlying causal structure on the various features of an image. To be
meaningful, generated perturbations need to satisfy constraints implied by the
causal model. We present a method for generating counterfactuals by
incorporating a structural causal …

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