AI Evidence Workflow
How AI review can support dataset approval while leaving evidence for later inspection.
Definition
An AI evidence workflow combines machine-generated review assistance with rule evidence and human approval records.
Problem
AI systems depend on data changes that are hard to review manually. Without evidence, teams cannot explain which dataset version changed behavior or why a release was approved.
Styxis perspective
Styxis builds software for sensitive data validation and AI governance, guided by Oath, Boundary, and Crossing: it binds responsibility to action, distinguishes operating states, and moves verified change into execution.
Product connection
Truthound Depot presents AI summaries, risk notes, reviewer checklists, Compare results, and Validation outcomes together before a reviewer accepts a Review Request, Publish, or Restore.
FAQ
Should AI approve dataset releases automatically?
For high-impact workflows, AI should assist review while gate evidence and human approval control the final promotion.
What evidence should be stored?
Store Data Version or Source identity, Compare results, Validation outcomes, reviewer decisions, AI summaries, Published Version identity, and artifact references.
How does this help AI evaluation?
It links model behavior changes back to the dataset version, evidence, and approval path that introduced the data.