Discovering and Mitigating Visual Biases through Keyword Explanation
Authors: Younghyun Kim, Sangwoo Mo, Minkyu Kim, Kyungmin Lee, Jaeho Lee, Jinwoo Shin

Addressing biases in computer vision models is crucial for real-world AI deployments. However mitigating visual biases is challenging due to their unexplainable nature often identified indirectly through visualization or sample statistics which necessitates additional human supervision for interpretation. To tackle this issue we propose the Bias-to-Text (B2T) framework which interprets visual biases as keywords. Specifically we extract common keywords from the captions of mispredicted images to identify potential biases in the model. We then validate these keywords by measuring their similarity to the mispredicted images using a vision-language scoring model. The keyword explanation form of visual bias offers several advantages such as a clear group naming for bias discovery and a natural extension for debiasing using these group names. Our experiments demonstrate that B2T can identify known biases such as gender bias in CelebA background bias in Waterbirds and distribution shifts in ImageNet-R/C. Additionally B2T uncovers novel biases in larger datasets such as Dollar Street and ImageNet. For example we discovered a contextual bias between \keyword bee  and \keyword flower  in ImageNet. We also highlight various applications of B2T keywords including debiased training CLIP prompting and model comparison.