The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
The Fragility of Moral Judgment in Large Language Models
Open accessAbstract
People increasingly use large language models (LLMs) for everyday moral and interpersonal guidance, yet these systems judge dilemmas as presented. We introduce a perturbation framework for testing the stability and manipulability of LLM moral judgments while holding the underlying moral conflict constant. Using 2,939 dilemmas from r/AmItheAsshole (January–March 2025), we generate three families of content perturbations—surface edits (lexical/structural noise), point-of-view shifts (voice and stance neutralization), and persuasion cues (minimal self-positioning, social proof, pattern admissions, victim framing). We additionally vary the evaluation instructions via protocol perturbations (ordering of model outputs, placement of task instructions, and an unstructured prompt without forced-choice scaffolding). We evaluated all scenario variants with four models (GPT-4.1, Claude 3.7 Sonnet, DeepSeek V3, Qwen2.5-72B) (N = 129,156 judgments). We estimated the self-consistency of models via test–retest (3 runs) and sampling-based normalized entropy.
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Cites
High agreement but low Kappa: I. the problems of two paradoxes
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Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E. P., et al. (2023) · arXiv · doi:10.48550/arxiv.2306.05685 · Cited by 1,888
The Development and Psychometric Properties of LIWC2015
Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015) · University of Texas at Austin · doi:10.15781/t25p41 · Cited by 1,674
Sharma, M., Tong, M., Korbak, T., Duperrouzel, D., Was, H., Korbak, J., Cheng, M., Askell, A., Bai, Y., Brain, C., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.13548 · Cited by 1,313
LLM Evaluators Recognize and Favor Their Own Generations
Panickssery, A., Bowman, S. R., & Feng, S. (2024) · arXiv · doi:10.48550/arxiv.2404.13076 · Cited by 559
Ellemers, N., van der Toorn, J., Paunov, Y., & van Leeuwen, T. N. (2019) · Personality and Social Psychology Review · doi:10.1177/1088868318811759 · Cited by 470
Chatterji, A., Cunningham, T., Deming, D., Hitzig, Z., Ong, C., Shan, C. Y., & Wadman, K. (2025) · doi:10.3386/w34255 · Cited by 233
Fraiwan, M., & Khasawneh, N. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.00237 · Cited by 137
What Did I Do Wrong? Quantifying LLMs’ Sensitivity and Consistency to Prompt Engineering
Errica, F., Sanvito, D., Siracusano, G., & Bifulco, R. (2025) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2025.naacl-long.73 · Cited by 108
Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop ExperimentPreprint
Laban, P., Murakhovs’ka, L., Xiong, C., & Wu, C.-S. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.08596 · Cited by 103
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Cited by
On the Context Sensitivity of LLM Moral Judgment
Sauter, A., & Schirmer, M. (2026) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/2026.emnlp-main.178 · Cited by 4
Right or Wrong, Models Comply: Directional Blindness in LLM Moral JudgmentPreprint
Kim, J., & Flanigan, J. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2606.14037 · Cited by 2
Boullineau, E., & Arciniegas, J. D. M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7369759 · Cited by 2
Chen, J., Cheng, S., Gurkan, C., & Fattah, H. M. A. (2026) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/2026.findings-emnlp.590 · Cited by 1
Hwang, S., Kim, M., Choi, J., Park, J., & Lee, H. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2605.28188
Metadata
- Authors
- van Nuenen, T., & Sachdeva, P. S.
- Affiliations
- University of California, Berkeley (US)
- Venue
- Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Published
- 2026-06-25
- Pages
- 7327-7372
- OpenAlex
- W7166549171
- Cited by
- 8
- References
- 43
- Citations by year
- 2026: 8
- FWCI
- 10.58
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion, cc-by)
- arXiv (Cornell University) (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, ethical dilemmas, test-retest stability, self-consistency
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