EU AI Act Article 13: transparency records for AI work

The direct answer

Article 13 of the EU AI Act addresses transparency and information for people affected. A work record can preserve what an AI-enabled process did and what its tools returned, but it is supporting evidence, not a compliance determination. Applicability depends on the system, role, and facts.

For eu ai act article 13, choose one question that a reviewer can answer from the record. Name the tool or source, the exact action, and the expected output before doing the work. This makes it easier to see whether the result actually answers the question or merely sounds plausible.

A practical way to apply this

1. Write the user's intended outcome in one sentence.
2. Identify which source, MCP tool, platform log, or human review can answer the question.
3. Run the smallest safe action and preserve its returned status, timestamp, and receipt link.
4. Check the receipt independently and label any evidence that is missing or outside the captured boundary.

A practical review starts with the official article text and the organization's role. Map each applicable requirement to the policy, system documentation, test evidence, and human review records that support it. Zambo can contribute execution-scoped tool records where those calls are part of the process. It cannot generate missing governance controls or replace an assessment by a qualified reviewer.

A concrete example

A real example is this public read-only BTC lookup receipt: https://zambo.dev/run/83ae4fd7-b9f8-497c-b842-72381c8ee6a8. Its verifier is https://zambo.dev/api/v2/receipt/83ae4fd7-b9f8-497c-b842-72381c8ee6a8/verify. The example shows one observed tool result; it is not evidence about eu ai act article 13 in your own system.

For a work-specific review, replace the sample with the receipt generated by your own call. Match the tool name and receipt ID exactly. If the output is a proposal, a blocked action, or an unavailable response, retain that state instead of describing it as completed work.

What the record can and cannot tell you

For transparency, capture what information was presented to users and the relevant system context. A receipt can show a tool execution, but it cannot establish that a user notice was adequate or actually understood.

A checkable receipt records what the connected execution service saved. It does not establish that a model's complete answer is accurate, that every possible input was tested, or that an outside service changed state. When an outside effect matters, look for a confirmation from the system that owns that state, such as a read-back, callback, or provider reference.

For sensitive work, publish only the fields needed for review. Keep access tokens, private credentials, customer data, and unrelated chat context out of a public handoff. Explain what the receipt covers and what remains outside its scope.

Review checklist

• Does the record identify the specific request and tool?
• Does the receipt ID match the result being discussed?
• Can a reviewer reach the verifier without the original chat?
• Are failures, retries, and pending steps represented honestly?
• Is outside confirmation linked to its actual source?
• Are private values excluded from the public copy?

Use this checklist for eu ai act article 13: transparency records for ai work and save only the evidence relevant to the decision. A short, well-scoped record is easier to review than a large transcript with no clear boundary.