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 Layered Cognitive Alignment Model (LCAM), a conceptual and normative framework for diagnosing interac-tional alignment failures in conversational AI. LCAM defines alignment as a calibrated fit among s...
| Authors | Manuele Reani, Hongyu Tian |
|---|---|
| Primary category | cs.HC |
| Published | 6 June 2026 |
| Last updated at source | 6 June 2026 |
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