Explainability Audit
Purpose
This page documents how system outputs are made explainable and auditable as a property of the system design. This layer exists to ensure that generated content can be traced back to retrieved material, extracted facts, and governing rules without reconstruction or guesswork.
In practice, this is the difference between being able to show exactly where an answer came from and having to justify it after the fact.
It defines the requirements for preserving lineage across retrieval, extraction, metadata application, and generation.
Scope
Includes source attribution, linkage between outputs and underlying data, and mechanisms for review, reproduction, and verification. Excludes model interpretability research, statistical explanations of model behavior, and any claims of transparency beyond documented data and rule lineage.
Constraints
- Every output must be traceable to retrieved, qualified source material and the rules that permitted its use.
- Attribution links are explicit, inspectable, and preserved through intermediate processing steps.
- Outputs that cannot be traced to their sources or governing rules are treated as invalid for audit or decision use.
- Audit mechanisms prioritize reproducibility and review over completeness, fluency, or convenience.
These constraints prevent confidence inflation by requiring that every claim remains defensible under inspection.
Notes
Explainability is a system property, not a post hoc feature. When auditability is retrofitted after generation, attribution gaps and unjustified confidence tend to emerge.