What It Means
An evidence payload shifts evaluation from 'is this output good?' to 'is this decision supported by this evidence under these rules?'
An evidence payload is the complete information package submitted when an AI decision is audited. It includes six components: (1) the evidence — the factual inputs the AI used, (2) the policy context — the regulations and rules governing the decision, (3) the AI-generated content — the actual decision produced, (4) the model trace — the reasoning chain the AI followed, (5) model metadata — version, confidence, validation history, and (6) redacted fields — sensitive data masked for privacy. Together, these components give reviewers everything they need to evaluate whether the AI's decision is correct, compliant, and adequately supported.
Why It Matters
Without an evidence payload, you're evaluating AI output in a vacuum. A denial letter might be perfectly written but completely wrong — the applicant might actually qualify. A credit decision might look reasonable but violate fair lending rules when you see the underlying data. The evidence payload provides the full context that makes meaningful evaluation possible. It's the difference between proofreading and auditing.
How Bookbag Helps
Bookbag's AI decision auditing framework is built around structured evidence payloads. Every AI decision submitted for review includes the complete evidence package — decision, evidence, policy, model trace, and metadata. Reviewers evaluate the decision against this full context, not just the output text. The evidence payload structure is standardized across industries while allowing industry-specific evidence fields.
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