What It Means
Message QA asks 'is this text good?' Decision auditing asks 'is this decision supported by this evidence under these rules?'
AI decision auditing extends quality evaluation from content (messages, emails) to decisions (eligibility determinations, credit approvals, claims adjudications, hiring recommendations). Instead of evaluating whether an AI's output reads well, decision auditing evaluates whether the decision is correct — meaning it's supported by the evidence, compliant with applicable regulations, and follows the proper reasoning chain. Each decision is submitted as a structured evidence payload and evaluated using an industry-specific taxonomy of failure categories, business impact ratings, and evidence sufficiency levels.
Why It Matters
AI systems increasingly make decisions that directly affect people — whether they get benefits, loans, insurance claims, or job offers. These decisions carry regulatory requirements, legal exposure, and real human consequences. When the AI gets it wrong, 'the model had 87% accuracy' doesn't help the person who was wrongly denied. Decision auditing creates the accountability layer: every decision is evaluated, documented, and correctable before it reaches the affected individual.
How Bookbag Helps
Bookbag's AI decision auditing framework provides structured evaluation for AI decisions across 14 regulated industries. Every decision is submitted as an evidence payload and evaluated using industry-specific taxonomy templates. Verdicts include failure categories, severity ratings, evidence sufficiency assessments, and corrected determinations. Every verdict produces an immutable audit trail and training data. The same platform handles both message QA and decision auditing.
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