We explore the Evidence Contact Test, a governance framework designed to ensure that AI-driven decisions are based on the actual inspection of underlying data rather than just persuasive summaries.
It highlights the risk of false evidentiary posture, where a system or human acts as if they have verified information that is actually missing, outdated, or unexamined.
To combat this, we propose a six-question audit and an Edge-Case Ledger to preserve visibility for outliers and contradictory facts that compression often erases. The methodology emphasizes retraceable compression, ensuring that every automated recommendation maintains a direct, verifiable path back to its original source material.
Furthermore, we warn against criteria collapse, where users mistake the professional formatting of AI output for factual accuracy.
The framework advocates for a risk-tiered approach to governance that prioritizes substantive evidence contact over mere throughput or formal approval clicks.
Full article available here
Evidence Frame Integrity – The Evidence Contact Test
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Aug 23
Our previous article in the ‘Evidence Frame Integrity’ series left a useful object on the table: the Edge-Case Ledger. Its purpose was to stop consequential exceptions from vanishing when evidence is compressed into a summary, score, shortlist or dashboard. The ledger asks what the smooth centre of the story left behind: outliers, minority cohorts, cont…
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