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arXiv · 2505.10231

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

Haozhe Luo·Ziyu Zhou·Zixin Shu·Aurélie Pahud de Mortanges·Robert Berke·Mauricio Reyes
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Abstract

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 at https://github.com/Roypic/Aligner.

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Haozhe Luo, Ziyu Zhou, Zixin Shu, Aurélie Pahud de Mortanges, Robert Berke, Mauricio Reyes. 2025-05-15. On the Interplay of Human-AI Alignment,Fairness, and Performance Trade-offs in Medical Imaging. https://arxiv.org/abs/2505.10231

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