LANDMARK · 6 MIN · STEWARD
Building evidence packs
After this landmark, you can describe what an evidence pack for an AI system needs to contain, and why assembling it after the fact is much harder than maintaining it continuously.
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This concept is shared, but the Everyday lens is less central here.
You can still explore it. We’re showing the shared explanation and a related practical view without hiding the knowledge.
An evidence pack is the collected documentation that demonstrates a system was built and operated responsibly: the risk classification and rationale, evaluation and testing results, the deployment and monitoring plan, incident history and how each was handled, and records of human oversight actually being exercised, not just designed. Organizations that maintain this continuously (updating it as the system changes) can answer a regulator’s, auditor’s, or investigative journalist’s question quickly and credibly; organizations that only think about evidence when asked scramble to reconstruct records that may not have been kept in the first place, which reads, accurately, as a governance gap.
This is the canonical concept. It stays the same across learner lenses so personalization never changes the underlying facts.
◌ EVERYDAY LENS
What this looks like for you
Not your layer directly, but this is what stands behind a company’s public claim that an AI system was ‘tested for bias’ or ‘reviewed for safety’; a real claim has an evidence pack behind it; an unsubstantiated one usually doesn’t.
MAKE A DECISION
A journalist asks a company to demonstrate that its AI hiring tool was tested for bias before deployment. The team knows testing was done a year ago but can’t quickly locate the results. What does this reveal?
CARRY THISFor one AI system your organization operates, check: could you quickly produce evidence of its testing, monitoring, and oversight if asked today? If not, what’s missing?