LCAM: A Framework for Diagnosing Interactional Alignment Failures in Con-versational AI
Conversational AI is increasingly used for advice, interpretation, reassurance, and decision support in contexts where users may be vulnerable, uncertain, or dependent on the system's apparent competence. Existing alignment work often focuses on model objectives, preference optimization, or output correctness. Yet, many harms arise through interaction: how systems frame authority, express uncertainty, simulate empathy, support reasoning, and make boundaries legible. This paper introduces the Lay
Record details
Published: 6 June 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare
Retrieved: 14 July 2026
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ethics.ai (6 June 2026), “LCAM: A Framework for Diagnosing Interactional Alignment Failures in Con-versational AI,” evidence record 1324, https://ethics.ai/record/1324 (originally published by arXiv).
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