AINeutralarXiv – CS AI · Jun 96/10
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The Governance of Human-LLM Interaction: Safety Gating, Civility Steering, and Affective Default Lock-In
Researchers introduce a framework for evaluating how LLM providers control user interaction styles through alignment mechanisms, measuring prompt steerability and regression-to-default behaviors across dialogue. The study reveals that provider-side controls shape not just safety but also communicative defaults that influence user autonomy, with implications for pluralism and democratic agency in human-AI systems.