Using Artificial Intelligence to Identify State Secrets
This work addresses the challenge of managing national security information for government agencies, though it is incremental as it applies existing machine learning methods to new data.
The researchers tackled the problem of identifying classified information in government records by analyzing nearly a million State Department cables from the 1970s using machine learning, achieving 90% identification of classified cables with less than 11% false positives. Their results also revealed issues with overclassification and underclassification, highlighting the need for improved reliability in classification practices.
Whether officials can be trusted to protect national security information has become a matter of great public controversy, reigniting a long-standing debate about the scope and nature of official secrecy. The declassification of millions of electronic records has made it possible to analyze these issues with greater rigor and precision. Using machine-learning methods, we examined nearly a million State Department cables from the 1970s to identify features of records that are more likely to be classified, such as international negotiations, military operations, and high-level communications. Even with incomplete data, algorithms can use such features to identify 90% of classified cables with <11% false positives. But our results also show that there are longstanding problems in the identification of sensitive information. Error analysis reveals many examples of both overclassification and underclassification. This indicates both the need for research on inter-coder reliability among officials as to what constitutes classified material and the opportunity to develop recommender systems to better manage both classification and declassification.