[Submitted on 17 Jul 2026]
Title:Beyond Right and Wrong: Evaluating Second-order Social Reasoning in Large Language Models
View a PDF of the paper titled Beyond Right and Wrong: Evaluating Second-order Social Reasoning in Large Language Models, by Sunny Rai and 8 other authors
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Abstract:Previous AI alignment efforts have focused primarily on first-order social norms -- teaching models what is socially acceptable or unacceptable (e.g., `do not steal'). However, social intelligence depends not only on norm recognition, but also on anticipating who will enforce it and how (e.g., public shame or even imprisonment). These second-order expectations, known as metanorms, govern how people respond when social rules are broken. We introduce a novel framework for evaluating metanorm reasoning in Large Language Models (LLMs) along two dimensions: emotional appraisal and behavioral response, and propose new classification tasks, namely, predicting self-regulation in violators, and other-regulation in observers. We release a multi-perspective dataset, NormReact, of 450 norm violation scenarios, hand-annotated for emotions and behavioral responses across norm violators' gender and observers' social closeness. Current LLMs portray a harsher social world: across six models, they overpredict negative sanctions where humans would expect inaction, and alignment with human judgments deteriorates as social distance increases. These findings suggest that AI systems in norm-sensitive domains from conflict mediation to policy simulation, may risk producing a distorted picture of social regulation: one that over-represents punishment and under-represents the tolerance, restraint, and relational calibration that characterize actual norm enforcement in real world.
Subjects:
Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
Cite as: arXiv:2609.05437 [cs.AI]
(or arXiv:2609.05437v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.05437
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sunny Rai [view email] [v1] Fri, 17 Jul 2026 19:30:58 UTC (12,940 KB)
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