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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 article studies one aspect of that problem by first creating a new public test collection with annotations for one class of exempt material subject to 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, even difficult cases can often be reliably detected. However, results also show that differences in reviewer interpretations, differences in record custodians, and differences in topics of the records used for training and testing can pose challenges.

acm journal on computing and cultural heritage/journal on computing and cultural heritage

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