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In the digital age, government agencies are grappling with unprecedented volumes of data, presenting challenges in effectively managing, accessing and declassifying information.

The State Department is no exception. According to Eric Stein, deputy assistant secretary for the Office of Global Information Services, the departments eRecords archive system currently contains more than 4 billion artifacts, which includes emails and cable traffic. The latter is how we communicate to and from our embassies overseas, Stein said.

Over time, however, department officials need to declare what can be released to the public and what stays classified a time-consuming and labor-intensive process.

The State Department has turned to cutting-edge technologies like artificial intelligence (AI) and machine learning (ML) to find a more efficient solution. Through three pilot projects, the department has successfully streamlined the document review process for declassification and improved the customer experience when it comes to FOIA (Freedom of Information Act) requests.

An ML-driven declassification effort

At the root of the challenge is Executive Order 13526, which requires that classified records of permanent historical value be automatically declassified after 25 years unless a review determines an exemption. For the State Department, cables are among the most historically significant records produced by the agency. However, current processes and resource levels will not work for reviewing electronic records, including classified emails, created in the early 2000s and beyond, jeopardizing declassification reviews starting in 2025.

Recognizing the need for a more efficient process, the department embarked on a declassification review pilot using ML in October 2022. Stein came up with the pilot idea after participating in an AI Federal Leadership Program supported by major cloud providers, including Microsoft.

For the pilot, the department used cables from 1997 and created a review model based on human decisions from 2020 and 2021 concerning cables marked as confidential and secret in 1995 and 1996. The model uses discriminative AI to score and sort cables into three categories: those it was confident should be declassified, those it was confident shouldnt be declassified, and those that needed manual review.

According to Stein, for the 1997 pilot group of more than 78,000 cables, the model performed the same as human reviewers 97% to 99% of the time and reduced staff hours by at least 60%.

We project [this technology] will lead to millions of dollars in cost avoidance over the next several years because instead of asking for more money for human resources or different tools to help with this, we can use this technology, Stein explained. And then we can focus our human resources on the higher-level and analytical thinking and some of the tougher decisions, as opposed to what was a very manual process.

Turning attention to FOIA

Building on the success of the declassification initiative, the State Department embarked on two other pilots to enhance the Freedom of Information Act (FOIA) processes from June 2023 to February 2024.

Like cable declassification efforts, handling a FOIA request is a highly manual process. According to Stein, sometimes those requests are a single sentence; others are multiple pages. But no matter the length, a staff member must acknowledge the request, advise whether the department will proceed with it, and then manually search for terms in those requests in different databases to locate the relevant information.

Using the lessons learned from the declassification pilot, Stein said State Department staff realized there was an opportunity to streamline certain parts of the FOIA process by simultaneously searching what was already in the departments public reading room and in the record holdings.

If that information is already publicly available, we can let the requester know right away, Stein said. And if not, if there are similar searches and reviews that have already been conducted by the agency, we can leverage those existing searches, which would result in a significant savings of staff hours and response time.

Beyond internal operations, the State Department also sought to improve the customer experience for FOIA requesters by modernizing its public-facing website and search functionalities. Using AI-driven search algorithms and automated request processing, the department aims to find and direct a customer to existing released documents and automate customer engagement early in the request process.

Lessons learned

Since launching the first pilot in 2022, team members have learned several things. The first is to start small and provide the space and time to become familiar with the technology. There are always demands and more work to be done, but to have the time to focus and learn is important, Stein said.

Another lesson is the importance of collaboration. Its been helpful to talk across different communities to not only understand how this technology is beneficial but also what concerns are popping upand discussing those sooner than later, he said. The sooner that anyone can start spending some time thinking about AI and machine learning critically, the better.

Another lesson is to recognize the need to continuously train a model because you cant just do this once and then let it go. You have to constantly be reviewing how were training the model (in light of) world events and different things, he said.

These pilots have also shown how this technology will allow State Department staff to better respond to other needs, including FOIA requests. For example, someone may ask for something in a certain way, but thats not how its talked about internally.

This technology allows us to say, Well, they asked for this, but they may have also meant that, Stein said. So, it allows us to make those connections, which may have been missing in the past.

The State Departments strategic adoption of AI and ML technologies in records management and transparency initiatives underscores the transformative potential of these tools. By starting small, fostering collaboration and prioritizing user-centric design, the department has paved the way for broader applications of AI and ML to support more efficient and transparent government operations.

The report was produced by Scoop News Group for FedScoop, as part of aseries on innovation in government, underwritten byMicrosoft Federal.To learn more about AI for government from Microsoft,sign up hereto receive news and updates on how advanced AI can empower your organization.

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How the State Department used AI and machine learning to revolutionize records management - FedScoop