Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals

@article{Dash2020EvaluatingAM,
  title={Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals},
  author={Saloni Dash and Vineeth N. Balasubramanian and Amit Sharma},
  journal={2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  year={2020},
  pages={3879-3888}
}
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 nontrivial 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… 
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