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Cited by 27Ethics and Social Impacts of AIPsychology of Moral and Emotional JudgmentEthics in Business and Education

The DOI matches its Crossref record; 86 of the 86 references it cites were found in a registry or an index.

Normative Evaluation of Large Language Models with Everyday Moral Dilemmas

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Sachdeva, Pratik, van Nuenen, Tom·2025·Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency
DOI 10.1145/3715275.3732044
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Abstract

The rapid adoption of large language models (LLMs) has spurred extensive research into their encoded moral norms and decision-making processes. Much of this research relies on prompting LLMs with survey-style questions to assess how well models are aligned with certain demographic groups, moral beliefs, or political ideologies. While informative, the adherence of these approaches to relatively superficial constructs tends to oversimplify the complexity and nuance underlying everyday moral dilemmas. We argue that auditing LLMs along more detailed axes of human interaction is of paramount importance to better assess the degree to which they may impact human beliefs and actions. To this end, we evaluate LLMs on complex, everyday moral dilemmas sourced from the "Am I the Asshole" (AITA) community on Reddit, where users seek moral judgments on everyday conflicts from other community members. We prompted seven LLMs to assign blame and provide explanations for over 10,000 AITA moral dilemmas. We then compared the LLMs' judgments and explanations to those of Redditors and to each other, aiming to uncover patterns in their moral reasoning. Our results demonstrate that large language models exhibit distinct patterns of moral judgment, varying substantially from human evaluations on the AITA subreddit. LLMs demonstrate moderate to high self-consistency but low inter-model agreement. Further analysis of model explanations reveals distinct patterns in how models invoke various moral principles. These findings highlight the complexity of implementing consistent moral reasoning in artificial systems and the need for careful evaluation of how different models approach ethical judgment. As LLMs continue to be used in roles requiring ethical decision-making such as therapists and companions, careful evaluation is crucial to mitigate potential biases and limitations.

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Cites

Research works printed but not linked: 7

  1. [n. d.]. The Claude 3 Model Family: Opus Sonnet Haiku. https://api.semanticscholar.org/CorpusID:268232499
  2. Logan Barnhart, Reza Akbarian Bafghi, Maziar Raissi, and Stephen Becker. [n. d.]. Aligning to What? Limits to RLHF Based Alignment. In Workshop on Responsibly Building the Next Generation of Multimodal Foundational Models.
  3. Maarten Buyl Alexander Rogiers Sander Noels Iris Dominguez-Catena Edith Heiter Raphael Romero Iman Johary Alexandru-Cristian Mara Jefrey Lijffijt and Tijl De Bie. 2024. Large Language Models Reflect the Ideology of their Creators. https://doi.org/10.48550/arXiv.2410.18417 arXiv:https://arXiv.org/abs/2410.18417. · DOI
  4. Paul F Christiano Jan Leike Tom Brown Miljan Martic Shane Legg and Dario Amodei. 2017. Deep reinforcement learning from human preferences. Advances in neural information processing systems 30 (2017).
  5. Stefano De Giorgis and Aldo Gangemi. 2023. That’s all Folks: A KG of values as commonsense social norms and behaviors. In International Workshop on Value Engineering in AI. Springer, 11–27.
  6. Zilin Ma, Yiyang Mei, and Zhaoyuan Su. 2024. Understanding the benefits and challenges of using large language model-based conversational agents for mental well-being support. In AMIA Annual Symposium Proceedings, Vol. 2023. 1105.
  7. Pratik S. Sachdeva and Tom van Nuenen. 2025. Normative Evaluation of Large Language Models with Everyday Moral Dilemmas. https://huggingface.co/datasets/ucberkeley-dlab/normative_evaluation_llms_everyday_dilemmas. Dataset accompanying the paper to appear in the 2025 ACM Conference on Fairness Accountability and Transparency (FAccT).

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Metadata

Authors
Sachdeva, P., & van Nuenen, T.
Affiliations
University of California, Berkeley (US)
ORCID
Venue
Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency
Publisher
ACM
Published
2025-06-23
Pages
690-709
DOI
10.1145/3715275.3732044
OpenAlex
W4411541864
Cited by
26
References
60
Citations by year
2026: 16 · 2025: 10
FWCI
9.27
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Psychology of Moral and Emotional Judgment
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Ethics in Business and Education
Keywords
large language models, moral judgment, blame attribution, moral reasoning, model evaluation, model explanation, inter-model agreement, self-consistency, ethical AI

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