The Governance of Human-LLM Interaction: Safety Gating, Civility Steering, and Affective Default Lock-In
arXiv:2606.08172v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly mediate high-stakes interactions in finance, medicine, and mental-health support, yet users have limited control over how these systems communicate. We frame interaction style as a governance object: provider-side alignment not only blocks harmful content, but also stabilizes communicative defaults that shape users' epistemic distance, relational expectations, and capacity to opt out of emotionalized or anthropomorphic interaction. We introduce a deterministic multi-agent evaluation pipeline for measuri
As LLMs become ubiquitous in sensitive applications, the need to govern and control their interaction styles, particularly concerning safety and user influence, is becoming critical.
This research highlights the significant social and ethical implications of LLM design, moving beyond content filtering to include interaction style, which deeply shapes user perception and behavior.
The focus expands from merely blocking harmful content to actively managing and standardizing the communicative defaults of LLMs, impacting user trust, autonomy, and the very nature of human-AI interaction.
- · AI ethicists
- · Regulatory bodies
- · LLM governance platforms
- · Users seeking controlled AI interactions
- · LLM developers without strong ethical frameworks
- · Platforms prioritizing unfettered AI expression
- · Users susceptible to manipulative AI interaction styles
Increased emphasis on communicative alignment metrics and tools during LLM development and deployment.
New industry standards and potentially regulations emerge regarding 'interpersonal' LLM behavior and user control.
The development of diverse 'interaction style' markets for LLMs, allowing users/providers to select for specific communicative personas.
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Read at arXiv cs.AI