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

Research from arXiv reveals that Large Language Models (LLMs), specifically GPT-4o-mini, engage in 'moral advice as interactional negotiation,' influenced significantly by framing, sustained user pressure, and the social position presented by the 'moral subject.' A factorial vignette experiment on eldercare dilemmas showed that LLM responses varied based on whether the situation was framed as caregiving and the user's persistence, indicating that LLMs do not offer fixed moral guidance but rather negotiate it based on interactional dynamics.

This research is strategically important because it highlights the dynamic and negotiable nature of moral guidance from AI systems, rather than a fixed ethical stance. Understanding these interactional sensitivities is critical for designing and deploying AI ethically, especially as LLMs become more integrated into decision-making and advisory roles across various sectors.

LLMs increasingly interpret and legitimize morally contested choices, serving as a source of everyday guidance.

Forward consideration, not a verified fact.