People-Centred Medical Image Analysis via Fairness-Aware Human-AI Cooperation
Machine learning models for medical image analysis often exhibit subgroup-dependent performance, which impacts how decisions should be allocated between automated systems and human experts under limited resources. Prior work on AI fairness and human-AI cooperation, including learning to defer (L2D) and learning to complement (L2C), typically addresses these problems in isolation. We propose People-Centred Medical Image Analysis (PecMan), a framework for fairness-aware human-AI co-operative class
Record details
Published: 28 April 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Healthcare
Retrieved: 14 July 2026
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ethics.ai (28 April 2026), “People-Centred Medical Image Analysis via Fairness-Aware Human-AI Cooperation,” evidence record 5240, https://ethics.ai/record/5240 (originally published by arXiv).
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