ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Logic and Reasoning in AI
Comparing AI and Human Moral Reasoning: Context-Sensitive Patterns Beyond Utilitarian Bias
Provisionally accepted
- 1University of Bojnord, Bojnurd, Iran
- 2Ferdowsi University of Mashhad, Mashhad, Iran
- 3Vanderbilt University, Nashville, United States
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Decision-making supported by intelligent systems is being increasingly deployed in ethically sensitive domains. As a result, it is of considerable importance to understand the patterns of moral judgments generated by large language models (LLMs). To this end, the current research systematically investigates how two prominent LLMs (i.e., ChatGPT and Claude Sonnet) respond to 12 moral scenarios previously administered to human participants (first language and second language users). The primary purpose was to examine whether the responses generated by LLMs align with either deontological or utilitarian orientations. Our secondary aim was to compare response patterns of these two models to those of human respondents in previous studies. Contrary to prevailing assumptions regarding the utilitarian tendency of LLMs, the findings revealed subtle response distributions of moral choice that are context-sensitive. Specifically, both models alternated between deontological and utilitarian judgements, depending on the scenario-specific features. These output patterns reflect complex moral trade-offs and may play a significant role in shaping societal trust and acceptance of AI systems in morally sensitive domains.
Keywords: artificial intelligence, deontology, Foreign language effect, Large language models, moral judgement, utilitarian
Received: 22 Sep 2025; Accepted: 04 Dec 2025.
Copyright: © 2025 Barabadi, Fotuhabadi, Arghavan and Booth. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Amanollah Arghavan
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