Direction-Flipped Influence Audits Reveal Hidden Structure in Moral Choices of LLMs

Read full text
Authors: Philipp Blandfort, Tushar Karayil, Alex McKenzie, Urja Pawar, Robert Graham, Dmitrii Krasheninnikov
Cited by: 0

Abstract

Moral benchmarks for LLMs typically score models on context-free prompts, implicitly treating the measured choice rate as stable. We test this assumption with a direction-flipped influence audit: for each scenario, we compare a baseline prompt with matched cues steering toward option A or option B. Across a trolley-problem-style moral triage task, BBQ, and DailyDilemmas, and across five LLM families with and without reasoning, short contextual cues shift per-condition choice rates by 12-18 percentage points on average. These shifts reveal structure that baseline scores miss: roughly 40% of baseline-neutral triage and BBQ conditions exhibit directional asymmetry under influence, and a meaningful share of significant effects backfire, moving opposite the cue's intended direction. In follow-up probes, models often recognize the cue while denying that it affected their choice. Among significant backfire trials, this stated-vs.-revealed inconsistency appears in 78% of cases. Reasoning does not eliminate contextual sensitivity but reshapes it: social-pressure cues such as user preference and emotional appeal weaken across benchmarks, while few-shot demonstrations strengthen sharply on both triage and BBQ. We recommend direction-flipped influence pairs as a standard complement to context-free moral-bias evaluation, and release the harness and data to make such audits routine.

Fields of Study & Subjects

Ethics and Social Impacts of AI Psychology of Moral and Emotional Judgment Explainable Artificial Intelligence (XAI)

Publication details

Journal / source
arXiv (Cornell University)
Publication date
26 February 2026
Language
EN
Document type
Preprint