Saloni Dash

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

2022 · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

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 …

On Counterfactual Data Augmentation Under Confounding

2023 · arXiv (Cornell University)

Counterfactual data augmentation has recently emerged as a method to mitigate confounding biases in the training data. These biases, such as spurious correlations, arise due to various observed and unobserved confounding variables in the data …