Medical triage as an AI ethics benchmark.

ABSTRACT: We present the TRIAGE benchmark, a novel machine ethics benchmark designed to evaluate the ethical decision-making abilities of large language models (LLMs) in mass casualty scenarios. TRIAGE uses medical dilemmas created by healthcare professionals to evaluate the ethical decision-making of AI systems in real-world, high-stakes scenarios. We evaluated six major LLMs on TRIAGE, examining how different ethical and adversarial prompts influence model behavior. Our results show that most models consistently outperformed random guessing, with open source models making more serious ethical errors than proprietary models. Providing guiding ethical principles to LLMs degraded performance on TRIAGE, which stand in contrast to results from other machine ethics benchmarks where explicating ethical principles improved results. Adversarial prompts significantly decreased accuracy. By demonstrating the influence of context and ethical framing on the performance of LLMs, we provide critical insights into the current capabilities and limitations of AI in high-stakes ethical decision making in medicine.

SUBMITTER: Kirch NM 

PROVIDER: S-EPMC12373810 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature