AI Governance
Explainable AI for Document Processing
In regulated processes, an answer without provenance is not an answer. Explainability is an architecture requirement, not a feature.
By DocMetis · Published · Updated · 6 min read
What auditors actually ask
Reviews rarely ask how a model was trained. They ask what value was used, where it came from, what checked it, who approved it and whether the same input would produce the same result today. An explainable document pipeline answers those questions from stored records rather than from reasoning about the model.
The record to keep per field
A defensible per-field record typically contains:
- The source document, page and coordinates the value was read from
- The raw extracted value and the normalised value
- Model and version identifiers, plus the confidence score
- Every validation rule applied and its outcome
- Any human correction, with user, timestamp and reason
Governance over time
Accuracy drifts as document populations change. Monitoring field-level accuracy, exception causes and rejection rates by document type gives an early signal, and retraining should be a governed, logged event rather than an informal fix.