AI transparency is the practice of giving affected people and operators clear, relevant information about where AI is used, what it does, what data or limitations matter, and how to question or appeal an outcome. It does not require publishing every model detail or exposing security-sensitive information.
Transparency should match the audience and impact. A user may need disclosure and recourse, while an operator needs limits and monitoring. Responsible AI supports those decisions, and AI governance owns the disclosure. Useful transparency answers what a person can do next.
When does AI transparency matter?
The EU AI Act sets specific duties for certain systems and generated content. Content provenance can carry origin information. More technical detail is not automatically more transparent.
Frequently asked questions
What is AI transparency used for?
The EU AI Act sets specific duties for certain systems and generated content. Content provenance can carry origin information.
Does AI transparency require revealing source code?
No. It requires information appropriate to the audience, risk, and applicable law, not unlimited disclosure.
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