The study presents a benchmark of 208 rare‑disease clinical vignettes to evaluate how large language models (LLMs) handle ethically charged decision‑making. Across 11 state‑of‑the‑art LLMs, the models consistently favored justice—specifically equal resource allocation—over other bioethical principles such as beneficence, non‑maleficence, and autonomy. The authors also found that the framing of authority (committee vs. clinician vs. patient) influences which ethical principle the models prioritize, suggesting that institutional pressures may shape LLM decision support in rare‑disease care.
As large language models (LLMs) enter high-stakes domains such as healthcare, understanding their moral reasoning becomes essential. Decisions about scarce medical resources often hinge on judgments of responsibility, particularly when patients' own actions contribute to illness.
arXiv:2608. 05583v1 Announce Type: cross Abstract: As large language models (LLMs) enter high-stakes domains such as healthcare, understanding their moral reasoning becomes essential.
arXiv:2608.21409v1 Announce Type: cross Abstract: In medicine, claims remain valid when supported by empirical evidence grounded in stable biological reality. In law, by contrast, truth is contingent...
arXiv:2608. 08061v1 Announce Type: new Abstract: The key question in moral judgement is not simply whether someone chooses the "right" answer, but how they decide what matters most when moral principles conflict.
arXiv:2608. 15424v1 Announce Type: cross Abstract: The rapid adoption of large language models has enabled the development of clinical multi-agent systems (MAS) capable of integrating multimodal patient data and supporting increasingly complex clinical decision-making.
The paper proposes an empirical pipeline to estimate the preferences that a large language model (LLM) implicitly optimizes by combining the model’s probability distribution over unknowns with its chosen action, and fitting a discrete choice model to recover the underlying cost function. This revealed-preference framework enables rigorous assessment of whether LLMs act consistently toward a goal, can articulate objectives that align with their decision policy, and can be steered by prompting to follow a user-specified cost function. Experiments across four medical diagnosis domains and various frontier and open-source models show that while many LLMs exhibit internal coherence, they still struggle to accurately report or adopt preferences when guided by users.
arXiv:2608. 12368v1 Announce Type: new Abstract: Agreement with human judgments is a common proxy for evaluating the alignment of large language models (LLMs).
The paper investigates how the inference setup of large language models (LLMs) influences their behavior in a medical resource‑allocation scenario. By comparing paired‑context and independent‑inference experiments, the authors show that adding a single contrasting patient sentence can shift the model’s probability assignments in opposite directions across most tested models. Additional experiments varying scenario attributes further demonstrate that patient information can have context‑dependent effects on LLM outputs.
arXiv:2608. 14630v1 Announce Type: cross Abstract: Human decision-making is often shaped by a range of well-documented cognitive biases.
arXiv:2601. 16529v4 Announce Type: replace Abstract: Large language models (LLMs) deployed in clinical decision support may acquiesce to patient requests for care that conflicts with evidence-based guidelines.
arXiv:2601. 21433v2 Announce Type: replace Abstract: Language models are increasingly consulted on ethically consequential questions, yet the stance a model expresses may not survive a change in framing.
arXiv:2606. 07237v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in healthcare for tasks such as clinical question answering, diagnosis support, and report summarization.