AI Output to Evidence

AI Output to Evidence

AI workflows often need human review after generation. The original model result and the later human judgment should remain distinguishable.

Recommended chain

source artifact → analysis run → immutable AI result → review event → validated or corrected result

Preserve the original model output

Record enough context to identify:

• source artifact;
• analysis run;
• model and relevant version;
• original model output;
• timestamp;
• integrity identity where applicable.

Add review separately

A reviewer may:

• confirm the output;
• correct part of it;
• reject it;
• request another analysis.

Represent that action as a separate event linked to the original result.

Why this matters

Overwriting the model output destroys evidence about what the system actually produced.

Keeping the original output immutable allows later reviewers to distinguish:

• model behavior;
• human correction;
• final accepted result.

This pattern is especially useful when verification, auditability, or regulated review requires a complete provenance chain.