What is an AI transparency report?

It is a public document that explains how an AI system works, where its data comes from, what it can and cannot do, how humans oversee it, and how users can contest decisions. It builds trust and helps meet emerging disclosure expectations and regulations.

Does this satisfy the EU AI Act?

Not by itself. The template covers many disclosure topics that align with transparency obligations, but specific regulatory requirements depend on your system's risk classification and jurisdiction. Treat it as a starting structure and confirm obligations with compliance experts.

How detailed should the report be?

Detailed enough to be honest and useful without exposing security-sensitive internals. Cover purpose, data, limitations, oversight, and recourse clearly; you can omit proprietary implementation details while still being transparent about behavior and risks.

How often should I update it?

Update whenever the model, training data, intended use, or oversight process materially changes, and review it at least annually. Stale transparency reports erode trust faster than none at all.

No. The template is assembled in your browser from your inputs using built-in structure. Nothing you type is uploaded or saved.

AI Transparency Report Template

AI transparency report template

Users, regulators, and partners increasingly expect a clear account of how your AI product works. The AI transparency report template turns a few details about your product into a structured, publishable skeleton — covering model usage, data sources, limitations, human oversight, and how people can appeal decisions — so you can fill in the specifics rather than start from a blank page.

How it works

You enter your product name, the AI systems it uses, who your users are, and notes on oversight and limitations. The tool assembles a sectioned report with your details inserted and bracketed placeholders where you need to add specifics. Sections follow widely-recognized transparency dimensions — purpose, data provenance, performance and limitations, human-in-the-loop controls, risk mitigations, and recourse. It is generated locally in the browser; nothing is uploaded.

Tips and notes

Be honest about limitations. Naming failure modes builds more trust than omitting them.
Describe behavior, not secrets. You can be transparent about what the system does without exposing exploitable internals.
Keep it current. Update the report whenever the model, data, or oversight process materially changes.
Map to your obligations. This is a starting structure — confirm specific regulatory requirements for your risk class and jurisdiction.