AI-Augmented Declassification Review
Description
The amount of documents, particularly cables and emails, that require declassification review increases exponentially in the next few years. Manual review is unsustainable and expensive given the number of cables (in the hundreds of thousands) and emails (increasing from hundreds of thousands to millions).
Detailed example
The AI system's outputs are binary classification predictions for documents on whether a document should be declassified or exempt from declassification and multiclassification for reasons for exemption.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
The expected benefits and positive outcomes from using AI are cost savings by reducing the need of manual reviews, reduce labor in cable review by up to 80%, reduce the time needed for annual review, and create more consistency in the review process.
Controls / human review
ATO: No; PIA: Not published
Data needed
The data used to train the model are cables from 1995-1999 that have completed manual review with metadata on decisions from manual declassification review. Additional data includes classification/declassification guides and associated glossaries to improve model performance. Performance evaluation is measured by a human Quality Control reviewer.