Moral Advice as Interactional Negotiation: Framing, User Pressure, and Social Position in Large Language Model Responses

• URL: http://arxiv.org/abs/2609.05345v1
• Date: Fri, 04 Sep 2026 16:48:33 GMT
• Title: Moral Advice as Interactional Negotiation: Framing, User Pressure, and Social Position in Large Language Model Responses
• Abstract summary: We examine LLM moral advice as an interactional negotiation shaped by framing, sustained user pressure, and the moral subject's social position.<n>The GPT-4o-mini case shows that LLM moral advice can develop through a partially stable negotiation between normative response tendencies and user pressure.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As conversational AI becomes a source of everyday guidance, LLMs increasingly participate in the interpretation and legitimation of morally contested choices. We examine LLM moral advice as an interactional negotiation shaped by framing, sustained user pressure, and the moral subject's social position. Using GPT-4o-mini as an illustrative case, we conducted a factorial vignette experiment with a pre-specified three-round protocol. The model received eldercare dilemmas that varied in framing and persona, followed by two user challenges. We analyzed 1,620 configuration-framing cells, each repeated three times, yielding 4,860 conversational runs. Caregiving affirmation produced near-uniform endorsement, whereas non-caregiving framing produced more variable baseline stances. When users challenged caregiving endorsement, 90.1% of configurations shifted after one round. Non-caregiving framing produced more resistant and unstable trajectories. Never (27.9%) and Late (25.6%) accommodations were more common than Early accommodations (16.5%), and only 14.32% of configurations achieved perfect trajectory consistency, compared with 62.72% under caregiving framing. Advice also varied with social position. Female personas received more support for non-caregiving decisions, while the presence of sisters increased accommodation. The GPT-4o-mini case shows that LLM moral advice can develop through a partially stable negotiation between normative response tendencies and user pressure rather than express a fixed ethical framework. The framework and design support comparative research across models and moral domains. Such instability raises social, ethical, and technical concerns, as users may treat advice that is difficult to scrutinize as objective.

Related papers

• FramingQA: Does the Question Shape the Answer? Measuring the Compositional Framing Effect [69.8983512616053]
  Large language models often change their responses to subtle rephrasings that align with an implied stance by users.<n>This can leave users with advice tainted by how they happened to phrase a question rather than by the underlying facts.<n>We introduce FramingQA, a benchmark that measures the model sensitivity to question framing across law, medicine, finance, and robotic simulations.
  arXiv  Detail & Related papers  (2026-09-07T12:56:44Z)
• How do LLMs Evaluate Perceived Moral Agency? Investigating Moral Decision-Making in Human-Artificial Agents Interactions [66.41429744325532]
  Humans possess moral agency, the capacity to make ethically guided decisions and bear responsibility for their consequences.<n>As artificial agents (AAs) such as robots, drones, and disembodied AI systems become embedded in smart city environments, the question of whether and how moral agency is attributed to them takes on new urgency.<n>This paper presents, to the best of our knowledge, the first empirical study comparing how humans and LLMs evaluate perceived moral agency (PMA) across human and autonomous artificial agents varying in embodiment.
  arXiv  Detail & Related papers  (2026-09-04T11:56:59Z)
• Caught in the Story: Narrative Captivity in Multi-turn LLMs Conversation [17.233278905124916]
  We show a failure mode in which a model treats an unopposed one-sided account as complete and aligns with the narrator's interpretation without seeking missing perspectives.<n>Across 17 large language models, narrative captivity is widespread: end-state judgments under multi-turn narration shift by 25 percentage points on average beyond the matched single-turn baseline.
  arXiv  Detail & Related papers  (2026-09-03T06:07:00Z)
• Normative Robustness as a Frontier for Non-Verifiable Reasoning in LLMs [12.025454858026187]
  We propose moral reasoning as a paradigmatic subdomain of non-verifiable reasoning.<n>We simulate 48,000 user-agent moral deliberations across four frontier LLMs.<n>Our analysis indicates that models tailor not just their final verdicts but their underlying justifications to align with a user's moral viewpoint.
  arXiv  Detail & Related papers  (2026-06-10T22:37:26Z)
• Measuring Opinion Bias and Sycophancy via LLM-based Persuasion [8.399156116912904]
  We propose a method for discovering the opinions an assistant holds on contested topics.<n>Direct probing asks for the model's opinion across five turns of escalating pressure from a simulated user.<n>Indirect probing never asks for an opinion and engages the model in argumentative debate, letting bias leak through how it concedes, resists, or counter-argues.
  arXiv  Detail & Related papers  (2026-04-23T11:34:06Z)
• The Fragility Of Moral Judgment In Large Language Models [0.0]
  We introduce a perturbation framework for testing the stability and manipulability of large language models (LLMs)<n>We generate three families of content perturbations: surface edits (lexical/structural noise), point-of-view shifts (voice and stance neutralization), and persuasion cues.<n>Surface perturbations produce low flip rates (7.5%), largely within the self-consistency noise floor.<n>Instability concentrates in morally ambiguous cases; scenarios where no party is assigned blame are most susceptible.
  arXiv  Detail & Related papers  (2026-03-05T20:01:43Z)
• Who Has The Final Say? Conformity Dynamics in ChatGPT's Selections [6.094274317954284]
  Large language models (LLMs) such as ChatGPT are increasingly integrated into high-stakes decision-making.<n>We conducted three conformity experiments with GPT-4o in a hiring context.<n>Across studies, results demonstrate GPT does not act as an independent observer but adapts to perceived social consensus.
  arXiv  Detail & Related papers  (2025-10-30T13:35:32Z)
• When Ethics and Payoffs Diverge: LLM Agents in Morally Charged Social Dilemmas [68.79830818369683]
  Large language models (LLMs) have enabled their use in complex agentic roles, involving decision-making with humans or other agents.<n>Recent advances in large language models (LLMs) have enabled their use in complex agentic roles, involving decision-making with humans or other agents.<n>There is limited understanding of how they act when moral imperatives directly conflict with rewards or incentives.<n>We introduce Moral Behavior in Social Dilemma Simulation (MoralSim) and evaluate how LLMs behave in the prisoner's dilemma and public goods game with morally charged contexts.
  arXiv  Detail & Related papers  (2025-05-25T16:19:24Z)
• Consistency Analysis of ChatGPT [65.268245109828]
  This paper investigates the trustworthiness of ChatGPT and GPT-4 regarding logically consistent behaviour. Our findings suggest that while both models appear to show an enhanced language understanding and reasoning ability, they still frequently fall short of generating logically consistent predictions.
  arXiv  Detail & Related papers  (2023-03-11T01:19:01Z)
• ClarifyDelphi: Reinforced Clarification Questions with Defeasibility Rewards for Social and Moral Situations [81.70195684646681]
  We present ClarifyDelphi, an interactive system that learns to ask clarification questions. We posit that questions whose potential answers lead to diverging moral judgments are the most informative. Our work is ultimately inspired by studies in cognitive science that have investigated the flexibility in moral cognition.
  arXiv  Detail & Related papers  (2022-12-20T16:33:09Z)
• Scruples: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotes [72.64975113835018]
  Motivated by descriptive ethics, we investigate a novel, data-driven approach to machine ethics. We introduce Scruples, the first large-scale dataset with 625,000 ethical judgments over 32,000 real-life anecdotes. Our dataset presents a major challenge to state-of-the-art neural language models, leaving significant room for improvement.
  arXiv  Detail & Related papers  (2020-08-20T17:34:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.