From FOIA Fiascos to AI Fixes: How Technology is Revolutionizing Transparency 

Nicholas Wittenberg 

The Freedom of Information Act (“FOIA”) is a cornerstone of governmental transparency, allowing the public to access federal records and hold agencies accountable.  However, the ever-increasing volume of electronic records, coupled with the complex nature of FOIA requests, has placed significant strain on agencies responsible for processing these requests.  Traditional manual review methods are no longer sufficient to keep pace with demand, leading to delays, backlogs, and inconsistent processing.  Fortunately, advances in Artificial Intelligence (“AI”) and generative AI offer unprecedented opportunities to modernize FOIA processing, making it more efficient and accurate. 

For years, the eDiscovery world has leveraged AI-driven technologies to enhance litigation and investigations.  While FOIA requests differ in purpose and process from litigation, they share fundamental similarities in managing vast amounts of data.  Just as plaintiffs in legal disputes have benefited from AI-powered eDiscovery tools, FOIA requestors—and the agencies responding to them—stand to gain significantly from integrating AI-driven solutions into the FOIA process. 

Lessons from eDiscovery: Automating Data Processing and Review 

The legal industry has long embraced AI-powered solutions to streamline document review, identify relevant materials, and manage large-scale data productions.  eDiscovery tools utilize machine learning, natural language processing (“NLP”), and predictive coding to reduce the burden on legal professionals and accelerate the review process.  These same principles can be applied to FOIA. 

Currently, many FOIA offices rely on outdated manual methods to review and redact records, a process that is both time-consuming and prone to human error.  AI can dramatically improve this workflow by: 

  • Automating document classification: AI models can categorize records by subject matter, sensitivity level, or responsiveness, reducing the time needed for initial sorting. 
  • Enhancing search capabilities: Advanced AI tools can understand the context of a request and retrieve relevant documents more efficiently than traditional keyword searches. 
  • Streamlining redaction: AI-driven redaction tools can identify and mask sensitive information (e.g., personally identifiable information (“PII”) or classified content) with greater accuracy and consistency than manual redaction. 
  • Improving quality control: AI models can flag inconsistencies in human review, ensuring that errors are minimized, and responses are more accurate. 

While eDiscovery solutions were historically used by law firms and corporations, plaintiffs in litigation are now leveraging these tools, making information exchange more seamless.  The shift toward best-practice eDiscovery productions—where data is efficiently organized and delivered—should serve as a model for FOIA processing.  If requestors and agencies both adopted eDiscovery solutions, transparency and efficiency in government disclosures would be significantly improved. 

The Need for Faster Government Contracting Cycles 

One of the biggest challenges in adopting AI for FOIA processing is the current pace of government procurement.  Traditional acquisition cycles—spanning from market research to Request for Information (“RFI”) to Request for Proposal (“RFP”)—often take two years or more.  By the time an agency finalizes a contract, the selected technology is already outdated, leading to inefficiencies and wasted resources. 

To address this issue, government contracting processes must be significantly tightened.  What currently takes two years should be compressed into two months, allowing agencies to deploy innovative AI solutions faster.  This requires: 

  • Streamlining market research and acquisition planning: Agencies must move beyond rigid bureaucratic processes and adopt more agile procurement strategies that enable rapid technology adoption. 
  • Emphasizing pilot programs: Small-scale AI deployments can provide proof-of-concept data, ensuring that agencies invest in solutions that demonstrably enhance FOIA processing before full-scale implementation. 
  • Encouraging collaboration with private sector innovators: The government should actively engage with AI technology providers, fostering partnerships that accelerate adoption and adaptation to agency needs. 

Tightening the FedRAMP Process to Improve AI Adoption 

Another significant barrier to adopting AI in government is the lengthy Federal Risk and Authorization Management Program (“FedRAMP”) certification process.  While FedRAMP is essential for ensuring the security and compliance of cloud-based technologies, the current approval timeline hampers the government’s ability to deploy AI solutions efficiently. 

AI tools must undergo rigorous security assessments before being used in government environments.  However, if the FedRAMP process is expedited, agencies can more quickly integrate AI-powered solutions into their FOIA workflows.  Key recommendations include: 

  • Reducing bureaucratic delays: Streamlining review processes and improving inter-agency coordination can cut unnecessary delays in FedRAMP approval. 
  • Creating AI-specific FedRAMP pathways: Given the unique nature of AI and machine learning tools, a specialized, fast-track approval process should be established. 
  • Encouraging competition: By lowering barriers to entry, more AI vendors can compete in the government space, driving innovation and ensuring agencies have access to the best available solutions. 

The Promise of Generative AI in FOIA Processing 

Generative AI holds immense promises for transforming FOIA administration.  Unlike traditional AI models, generative AI can analyze complex patterns in data and generate human-like text, making it highly effective for summarization, classification, and even drafting responses to FOIA requests. 

Potential applications of generative AI in FOIA include: 

  • Automated request summarization: AI can analyze incoming FOIA requests and generate concise summaries, allowing agencies to prioritize and allocate resources efficiently. 
  • Smart document synthesis: Generative AI can create executive summaries of lengthy reports, helping FOIA officers provide more digestible information to requestors. 
  • Enhanced training and compliance: AI-driven chatbots and virtual assistants can guide FOIA officers through best practices, improving consistency in decision-making and compliance with statutory requirements. 

A Future of Enhanced Efficiency and Transparency 

The integration of AI and generative AI into FOIA processing represents a significant step toward a more efficient, accurate, and transparent government.  By learning from the success of eDiscovery in litigation, agencies can modernize their FOIA workflows, reducing backlogs and improving responsiveness. 

To fully realize the benefits of AI, agencies must also address structural barriers, including slow procurement cycles and outdated certification processes.  Streamlining government contracting and expediting the FedRAMP approval process will ensure that agencies can adopt and deploy AI-driven solutions more effectively. 

In the same way that eDiscovery has revolutionized litigation, AI-powered FOIA tools have the potential to transform government transparency.  By embracing these technologies, agencies can better serve the public, ensuring that FOIA remains a powerful tool for accountability in the digital age. 

Nicholas Wittenberg is Corporate Counsel and Senior Advisor for Legal Technology and Innovation at Armedia.  Previous positions include serving Senior Counsel at the White House Office of Science and Technology Policy.  He currently serves on the FOIA Advisory Committee.