The DAREB© Test: Verifying the Chain of Proof Behind AI Outcomes

Most accountability frameworks look at how an AI system is intended to work in general terms. Can your architecture forensically reconstruct the exact logic behind one single disputed decision?

The DAREB framework flips the perspective of standard AI auditing. It starts at the absolute end of the chain, with a single real outcome affecting a single real person at one exact moment in time, and tracks the surviving evidence backwards.

When an automated decision faces a formal regulatory or legal challenge, high-level maturity scores mean nothing. You can either show the definitive chain of proof for that specific case, or you cannot.
Russell Parrott

The 5 Pillars of Evidentiary Accountability

When a machine-led outcome triggers a crisis, traditional system logs leave massive structural gaps. The DAREB test applies a brutal, binary standard to your records, verifying whether five distinct links survive intense scrutiny:

If any link in this sequence is missing, fragmented, or opaque, the chain of responsibility is broken. The decision cannot stand.

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The Director Accountability Test

Oversight habits designed for a slower world fail when automated systems operate at scale. When a failure triggers a crisis, investigators and D&O insurers look directly past corporate entities to examine individual board members.

This test uses five personal markers: Participation, Information, Understanding, Judgement and Evidence to determine if your individual actions stand up under hostile cross-examination.

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The EU PLD Exposure Test.

The revised Product Liability Directive brings strict liability rules straight to software, AI systems, and automated updates. If your system causes harm and a court orders an evidence disclosure, gaps in your technical logs create an automatic legal presumption that your AI was defective.

This 16-question framework maps your exposure across five critical structural vectors before a trial begins.

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