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

• URL: http://arxiv.org/abs/2505.10231v1
• Date: Thu, 15 May 2025 12:43:23 GMT
• Title: On the Interplay of Human-AI Alignment,Fairness, and Performance Trade-offs in Medical Imaging
• Authors: Haozhe Luo, Ziyu Zhou, Zixin Shu, Aurélie Pahud de Mortanges, Robert Berke, Mauricio Reyes,
• Abstract summary: We provide the first systematic exploration of Human-AI alignment and fairness in this domain.<n>Our results show that incorporating human insights consistently reduces fairness gaps and enhances out-of-domain generalization.<n>These findings highlight Human-AI alignment as a promising approach for developing fair, robust, and generalizable medical AI systems.
• Score: 3.054669417364281
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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