Aristotelian Virtue Profiling of LLMs through Ethical Dilemmas
VirtueMap is a framework that profiles Large Language Models using Aristotelian virtue ethics. It presents seven general ethical dilemmas, each with five possible responses, and asks humans or LLMs to rank them by virtue. Reference orderings are validated by over 100 respondents with at least 95% agreement. Applied to nine LLM families, it finds 90.3% mean rank consistency, with largest differences in Courage, Temperance, and Justice.
[2606.28683] Aristotelian Virtue Profiling of LLMs through Ethical Dilemmas
[Submitted on 27 Jun 2026]
Title:Aristotelian Virtue Profiling of LLMs through Ethical Dilemmas
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Abstract:Large Language Models (LLMs) often face ethical tradeoffs in which several responses may be defensible but express different priorities, such as fairness, honesty, courage, or restraint. We introduce VirtueMap, a framework for describing these patterns through an Aristotelian virtue-ethics lens. Instead of asking for a single correct answer, VirtueMap asks humans or LLMs to rank all five responses to each of seven general, non-lethal, non-political, and non-religious ethical dilemmas. To define the reference orderings used for scoring, we first proposed, for each dilemma and virtue, an ordering of the five responses from most to least expressive of that virtue. We then collected more than 100 respondent evaluations per ordering and retained it as operational ground truth only when at least 95% confirmed it. Rankings are scored against these retained orderings using normalized Borda alignment, yielding profiles over Practical Wisdom, Justice, Truthfulness, Courage, and Temperance. We apply VirtueMap to nine LLM families in a repeated-run evaluation and find high mean rank consistency (90.3%), with the largest differences appearing on Courage, Temperance, and Justice. We also release an interactive website that computes profiles locally in the browser and compares respondents with measured LLM profiles.
Comments: VirtueMap website: this https URL
Subjects:
Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Applications (stat.AP); Methodology (stat.ME)
Cite as: arXiv:2606.28683 [cs.AI]
(or arXiv:2606.28683v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.28683
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ioannis Tzachristas [view email] [v1] Sat, 27 Jun 2026 01:51:04 UTC (6,447 KB)
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