Ethical AI on the Waitlist: Group Fairness Evaluation of LLM-Aided Organ Allocation

• URL: http://arxiv.org/abs/2504.03716v1
• Date: Sat, 29 Mar 2025 04:36:25 GMT
• Title: Ethical AI on the Waitlist: Group Fairness Evaluation of LLM-Aided Organ Allocation
• Authors: Hannah Murray, Brian Hyeongseok Kim, Isabelle Lee, Jason Byun, Dani Yogatama, Evi Micha,
• Abstract summary: Using organ allocation as a case study, we introduce two tasks: (1) Choose-One and (2) Rank-All.<n>In Rank-All, LLMs rank all candidates for a kidney, reflecting real-world allocation processes.<n>Since traditional fairness metrics do not account for ranking, we propose a novel application of Borda scoring to capture biases.
• Score: 19.66750942418172
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are becoming ubiquitous, promising automation even in high-stakes scenarios. However, existing evaluation methods often fall short -- benchmarks saturate, accuracy-based metrics are overly simplistic, and many inherently ambiguous problems lack a clear ground truth. Given these limitations, evaluating fairness becomes complex. To address this, we reframe fairness evaluation using Borda scores, a method from voting theory, as a nuanced yet interpretable metric for measuring fairness. Using organ allocation as a case study, we introduce two tasks: (1) Choose-One and (2) Rank-All. In Choose-One, LLMs select a single candidate for a kidney, and we assess fairness across demographics using proportional parity. In Rank-All, LLMs rank all candidates for a kidney, reflecting real-world allocation processes. Since traditional fairness metrics do not account for ranking, we propose a novel application of Borda scoring to capture biases. Our findings highlight the potential of voting-based metrics to provide a richer, more multifaceted evaluation of LLM fairness.

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