On the Interplay of Human-AI Alignment,Fairness, and Performance Trade-offs in Medical Imaging

Deep neural networks excel in medical imaging but remain prone to biases, leading to fairness gaps across demographic groups. We provide the first systematic exploration of Human-AI alignment and fairness in this domain. Our results show that incorporating human insights consistently reduces fairness gaps and enhances out-of-domain generalization, though excessive alignment can introduce performance trade-offs, emphasizing the need for calibrated strategies. These findings highlight Human-AI alignment as a promising approach for developing fair, robust, and generalizable medical AI systems, striking a balance between expert guidance and automated efficiency. Our code is available atthis https URL.

@article{luo2025_2505.10231,
  title={ On the Interplay of Human-AI Alignment,Fairness, and Performance Trade-offs in Medical Imaging },
  author={ Haozhe Luo and Ziyu Zhou and Zixin Shu and Aurélie Pahud de Mortanges and Robert Berke and Mauricio Reyes },
  journal={arXiv preprint arXiv:2505.10231},
  year={ 2025 }
}