What It Means
A model trace records the step-by-step reasoning path an AI system followed to arrive at its decision. For a benefits eligibility determination, this might be: income verification → household size adjustment → federal poverty level calculation → deduction application → net income comparison → categorical eligibility check → determination. For a lending decision: credit pull → score validation → DTI calculation → LTV assessment → risk tier assignment → rate calculation → decision output. The trace doesn't need to expose proprietary model internals — it documents the logical sequence of operations that transformed inputs into the output decision.
Why It Matters
Without a model trace, you can see what the AI decided but not how it got there. This makes it impossible to identify where reasoning went wrong. Did the lending model skip the DTI calculation? Did the benefits model fail to apply standard deductions? The trace reveals exactly where in the reasoning chain the error occurred — which is essential for both correcting individual decisions and fixing systematic model issues.
How Bookbag Helps
Bookbag includes model trace as a standard component of the evidence payload. Reviewers can follow the AI's reasoning chain step by step and identify exactly where it deviated from correct procedure. When a verdict identifies a failure, the model trace shows where the failure occurred — not just what the incorrect output was.
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