Artificial intelligence transparency communicates what a system does, why it is being used and where its claims stop. It may include model and data documentation, explanations of uncertainty, disclosure of automated content and clear ownership of decisions.
Transparency should help a real user form an accurate understanding rather than merely satisfy a disclosure checklist. Teams can test this by asking unfamiliar users what they understood and comparing that response with the intended message. Any gap reveals information that needs clarification.
