An AI Policy Tool for Today: Ambitiously Invest in NIST

We believe that sensible artificial intelligence (AI) policy requires, among other things, the ability to accurately describe and quantify the capabilities and risks of AI systems. This ability is both an enabler and a prerequisite to effective regulation, as measurement tools allow us to objectively assess systems and ensure they meet appropriate safety thresholds. In this post, we propose a policy intervention for how to do this: ambitiously fund the National Institute of Standards and Technology (NIST) to support its AI measurement and standards efforts.

We were heartened by the bipartisan support for maintaining American leadership in the development of critical technologies, as expressed during the April 18 budget hearing on the 2024 Request for the Department of Commerce (and by extension, NIST). We think one of the best ways to channel that support is through an increase in federal funding for NIST so that it is well placed to carry out its work promoting safe technological innovation.

In this post, we give an overview of why we think this, and we also share a policy proposal for what an ambitious funding program for NIST could look like in practice. This proposal is readily actionable and builds on a solid foundation of existing work at the agency; we view it as a complement to a suite of policy levers for stronger AI governance.

Despite NIST’s foundational work in measurement, we’ve seen a general under-resourcing and concerning stagnation in funding for AI-related programs at NIST over the past few years. This lack of support is especially acute against the backdrop of recent technological progress and the widespread adoption of AI systems. With an ambitious investment, NIST could build on fundamental measurement techniques and standardize them across the field. Additional resourcing would also allow NIST to build much-needed community resources, such as testbeds, to assess the capabilities and risks of today’s open-ended AI systems.

In the course of just a few years, we’ve seen rapid technological progress in the kinds of tasks AI systems can complete, and a significant rise in the number of publicly deployed, AI-powered products. Researchers have also discovered that these systems display both significant capabilities and risks, some of which can emerge abruptly through training larger-scale models, and some of which are only discovered after deployment. In an attempt to address this, a variety of ideas have been put forward for how best to govern both the development and use of AI systems.

There have been calls for new federal agencies to regulate AI, multinational legislative proposals to mandate compliance requirements, and even calls from researchers to temporarily “pause” AI development. While these ideas all have meritable qualities, we believe there is also a simpler, “shovel ready” idea available to policymakers in the U.S. that receives comparatively little attention: increasing funding for NIST to build and refine measures of AI assurance.

NIST has for many years diligently worked on the science behind measuring AI systems and the development of associated technical standards. Some highlights of NIST’s work in this area include the Face Recognition Vendor Test and AI Risk Management Framework . AI researchers are also deeply familiar with MNIST , a handwriting recognition dataset which helped drive progress in computer vision in the 1990s and was based on NIST databases of handwritten characters and numbers.