Agreement Is Not Alignment: Divergent Moral Grounds in Human and LLM Ethical Judgments
This paper tests whether high agreement between LLMs and human annotators on moral judgment labels reflects shared moral reasoning. Using a curated 500-item ETHICS-derived benchmark spanning five moral domains, the authors collect human and model annotations of both final labels and supporting rationales. Although label agreement is often high, rationale-level analysis reveals systematic divergence in the moral grounds models invoke, even when labels match. The authors conclude that agreement should not be treated as alignment and that label-based evaluation can be misleadingly reassuring.
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[Submitted on 14 Jul 2026]
Title:Agreement Is Not Alignment: Divergent Moral Grounds in Human and LLM Ethical Judgments
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Abstract:Agreement with human judgments is a common proxy for evaluating the alignment of large language models (LLMs). Yet agreement in final labels does not show that human annotators and models rely on the same moral grounds. Two agents may reach the same judgment while appealing to different principles, contextual assumptions, or interpretations of the situation.
We test this distinction using a curated 500-item ETHICS-derived benchmark spanning five domains of moral judgment, with new human annotator and LLM annotations of both final labels and supporting rationales. Across frontier and open model families, agreement with human annotator majority labels is often high. However, rationale-level analysis reveals systematic divergence in the moral grounds expressed by human annotators and models. In particular, models redistribute attention across categories such as harm, respect, promise-keeping, justice, desert, and excuse relevance, even when their final labels match the human annotator majority.
Our results show that agreement should not be treated as equivalent to alignment. Label-based evaluation can therefore be misleadingly reassuring unless complemented by analysis of the reasons, principles, and moral priorities expressed in model judgments.
Comments: 9 pages, 4 figures, 3 tables. Accepted and presented at the AI Transparency Conference (AITC 2026), Nuremberg, Germany, June 5-6, 2026
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.12368 [cs.AI]
(or arXiv:2608.12368v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.12368
arXiv-issued DOI via DataCite
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From: Octavian M. Machidon [view email] [v1] Tue, 14 Jul 2026 09:24:14 UTC (502 KB)
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