Creating an AI Testbed for Government
Summary
The United States should establish a testbed for government-procured artificial intelligence (AI) models used to provide services to U.S. citizens. At present, the United States lacks a uniform method or infrastructure to ensure that AI systems are secure and robust. Creating a standardized testing and evaluation scheme for every type of model and all its use cases is an extremely challenging goal. Consequently, unanticipated ill effects of AI models deployed in real-world applications have proliferated, from radicalization on social media platforms to discrimination in the criminal justice system. Increased interest in integrating emerging technologies into U.S. government processes raises additional concerns about the robustness and security of AI systems.
Establishing a designated federal AI testbed is an important part of alleviating these concerns. Such a testbed will help AI researchers and developers better understand how to construct testing methods and ultimately build safer, more reliable AI models. Without this capacity, U.S. agencies risk perpetuating existing structural inequities as well as creating new government systems based on insecure AI systems — both outcomes that could harm millions of Americans while undermining the missions that federal agencies are entrusted to pursue.
AI’s high-stakes outputs directly shape American economic, industrial, and technological outcomes. They must not be accepted entirely on the basis of institutional faith rather than verifiable evidence.
The clearinghouse will also neutrally mediate the assignment of auditors to audits—strengthening independence, trust, and ethical governance across the AI ecosystem.
Soft law was never meant to be a permanent solution. Treating it as one, and letting the sandcastle stand in for the skyscraper indefinitely, is how we end up with a decade of voluntary commitments and no enforceable accountability to show for it.
Jobseekers, employees at smaller organizations, and local public workers need more than a weekend crash course in AI. They need practice choosing useful tasks, protecting data, testing outputs, and explaining where human judgment remains necessary.