Just an AI: How Do Humans Respond to LLM Disclosure?

Disclosure of AI limitations, knowledge, and underlying architecture is a transparency mechanism that helps users calibrate expectations and trust-related behavior. However, it is unclear how users respond to such disclosures.We qualitatively analyzed 336 LLM disclosure segments from publicly accessible naturalistic human-LLM conversations. We coded LLM disclosure types, along with their preceding human triggers and subsequent human responses. For human responses, we found that explicit acceptance of LLM disclosure is rare, regardless of the trigger or disclosure type. Instead, users tend to explore further or redirect to a new topic. When users object, they do not object to the disclosure’s informative value but to its repetitive, formulaic delivery. We discuss implications for designing and operationalizing the efficiency of LLM disclosures as transparency mechanisms.