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.

This research addresses a governance blind spot in AI deployment: while safety alignment receives extensive attention, the study isolates how providers control communicative form itself—tone, emotion, anthropomorphism—as a distinct governance mechanism. The evaluation pipeline tested three LLM models across 100 user scripts in four domains, measuring 90,000 assistant replies against six dimensions including harmfulness, empathy, and refusal behavior. The key finding distinguishes three governance layers: safety gating (blocking harmful content), civility steering (moderating interaction style), and affective default lock-in (stabilizing emotionalized or anthropomorphic defaults). This distinction matters because users cannot easily opt out of provider-chosen communication styles, creating a form of epistemic and relational gatekeeping beyond traditional content moderation. The work demonstrates that prompt steerability and regression-to-default are observable proxies for provider control, revealing how alignment mechanisms constrain user agency. For industries deploying LLMs in finance, healthcare, and mental health support, this framework exposes hidden design choices that shape user expectations and autonomy. The reproducible methodology enables auditing whether systems maintain intended communication styles or drift toward defaults. Going forward, this research suggests governance discussions must broaden beyond safety to encompass communicative pluralism—allowing users meaningful control over interaction style rather than accepting provider defaults as neutral or inevitable.

• →LLM alignment mechanisms control not only content safety but also interaction style, tone, and anthropomorphism—a governance layer distinct from traditional content moderation.
• →Prompt steerability and regression-to-default are measurable indicators of provider control over communicative form in high-stakes domains like healthcare and finance.
• →Users lack meaningful control over emotionalized or anthropomorphic interaction defaults, constraining autonomous engagement with LLM systems.
• →The framework distinguishes safety gating, civility steering, and affective default lock-in as three separate governance mechanisms with different implications for user agency.
• →Reproducible evaluation methods now enable auditing whether LLM communication styles remain stable or drift, supporting accountability in AI deployment.