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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The Development and Psychometric Properties of LIWC2015
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LLM Evaluators Recognize and Favor Their Own Generations
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What Did I Do Wrong? Quantifying LLMs’ Sensitivity and Consistency to Prompt Engineering
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Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop ExperimentPreprint
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Cited by
On the Context Sensitivity of LLM Moral Judgment
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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
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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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- van Nuenen, T., & Sachdeva, P. S. (2026). The Fragility of Moral Judgment in Large Language Models. Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency. https://doi.org/10.1145/3805689.3806447
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