Auditing Political Alignment in LLM Assistants: Engagement, Stance, and User Identity
LLM-based AI systems answer political questions for hundreds of millions of people. Current audits measure what they say to an average user, but their behavior is dynamic. I argue that their political behavior is a set of policies over whom to answer, what to say, and whether to engage at all, conditional on the topic and what the system knows about the user. I call these policies the system's speech regime, which is how a developer settles the tradeoff between answering, accommodating the user, and refusing, each of which carries a cost that varies by topic. I derive a typology of five regimes from two dimensions, engagement and stance. I test six AI systems (OpenAI, Anthropic, xAI, Goog...
| Authors | Joan C. Timoneda |
|---|---|
| Primary category | cs.CL |
| Published | 19 September 2026 |
| Last updated at source | 19 September 2026 |
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