MEDebiaser: A Human-AI Feedback System for Mitigating Bias in Multi-label Medical Image Classific...

MEDebiaser: A Human-AI Feedback System for Mitigating Bias in Multi-label Medical Image Classific... Shaohan Shi, Yuheng Shao, Haoran Jiang, Yunjie Yao, Zhijun Zhang, Xu Ding, Quan Li UIST 2025: The 38th Annual ACM Symposium on User Interface Software and Technology Session: 4. Searching, Classifying and Understanding Data Medical images often contain multiple labels with imbalanced distributions and co-occurrence, leading to bias in multi-label medical image classification. Close collaboration between medical professionals and machine learning practitioners has significantly advanced medical image analysis. However, traditional collaboration modes struggle to facilitate effective feedback between physicians and AI models, as integrating medical expertise into the training process via engineers can be time-consuming and labor-intensive. To bridge this gap, we introduce MEDebiaser, an interactive system enabling physicians to directly refine AI models using local explanations. By combining prediction with attention loss functions and employing a customized ranking strategy to alleviate scalability, MEDebiaser allows physicians to mitigate biases without technical expertise, reducing reliance on engineers, and thus enhancing more direct human-AI feedback. Our mechanism and user studies demonstrate that it effectively reduces biases, improves usability, and enhances collaboration efficiency, providing a practical solution for integrating medical expertise into AI-driven healthcare. DOI:: doi.org/10.1145/3746059.3747725 Web:: https://programs.sigchi.org/uist/2025... Video presentations for UIST 2025 papers