Providing More Efficient Access To Government Records: A Use Case Involving Application of Machine Learning to Improve FOIA Review for the Deliberative Process Privilege

TL;DR

This paper explores using machine learning to automate FOIA review for deliberative process privilege exemptions, aiming to reduce manual effort and backlog in government record disclosures.

Contribution

It introduces a new annotated test collection for exempt material and evaluates current text classification methods for identifying such records.

Findings

High accuracy in identifying exempt records within the same reviewer context

Reviewer interpretation variability affects classification reliability

Differences in record topics between training and testing impact performance

Abstract

At present, the review process for material that is exempt from disclosure under the Freedom of Information Act (FOIA) in the United States of America, and under many similar government transparency regimes worldwide, is entirely manual. Public access to the records of their government is thus inhibited by the long backlogs of material awaiting such reviews. This paper studies one aspect of that problem by first creating a new public test collection with annotations for one class of exempt material, the deliberative process privilege, and then by using that test collection to study the ability of current text classification techniques to identify those materials that are exempt from release under that privilege. Results show that when the system is trained and evaluated using annotations from the same reviewer that even difficult cases can often be reliably detected, but that differences in reviewer interpretations, differences in record custodians, and that differences in topics of the records used for training and testing pose additional challenges.