The Fragility of Moral Judgment in Large Language Models
Year: 2026
Type: conference-paper
Abstract: 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.
Authors: Tom van Nuenen, Pratik Singh Sachdeva
Institution: University of California, Berkeley
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166549171