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Beyer, Eshoo Introduce Landmark AI Regulation Bill

U.S. Representatives Don Beyer (D-VA) and Anna Eshoo (D-CA), who serve as Vice Chair and Co-Chair, respectively, of the Congressional Artificial Intelligence (AI) Caucus, today introduced the AI Foundation Model Transparency Act, ambitious legislation to promote transparency in artificial intelligence foundation models.

Foundation models are AI models trained on broad data; they power the generative AI websites and chatbots that have drawn international focus over the past year. Information about the data these models are trained on generally is not available to the public, and AI models often produce inaccurate, imprecise, or biased responses due to limitations or biases in the model’s training data or how the model was trained. This often results in racial or gendered bias, which can have serious real-world impacts in areas including health-related AI inferences, loan granting, housing approval, or predictive policing.

The AI Foundation Model Transparency Act would direct the Federal Trade Commission (FTC), in consultation with the National Institute of Standards and Technology (NIST) and the Office of Science and Technology Policy (OSTP), to set standards for what information high-impact foundation models must provide to the FTC and what information they must make available to the public. Information identified for increased transparency would include training data used, how the model is trained, and whether user data is collected in inference.

“Artificial intelligence foundation models commonly described as a ‘black box’ make it hard to explain why a model gives a particular response. Giving users more information about the model—how it was built and what background information it bases its results on—would greatly increase transparency,” said Beyer. “This bill would help users determine if they should trust the model they are using for certain applications, and help identify limitations on data, potential biases, or misleading results. When a model’s bias could lead to harmful results like rejections for housing or loan applications, or faulty medical decisions, the importance of this reform becomes clear and very significant.”

“AI offers incredible possibilities for our country, but it also presents peril. Transparency into how AI models are trained and what data is used to train them is critical for consumers and policy makers,” said Eshoo. “The AI Foundation Model Transparency Act directs the Federal Trade Commission and NIST to establish standards for data sharing by foundation model deployers. This critical legislation will provide necessary information and empower consumers to make well informed decisions when they interact with AI. It will also provide the FTC critical information for it to continue to protect consumers in an AI-enabled world.”

The AI Foundation Model Transparency Act would:

  • Direct the FTC, in consultation with NIST, the Copyright Office, and OSTP, to set transparency standards for foundation model deployers, by asking them to make certain information publicly available to consumers;
  • Direct companies to provide consumers and the FTC with information on the model’s training data, model training mechanisms, and whether user data is collected in inference; and
  • Protect small deployers and researchers, while seeking responsible transparency practices from our highest-impact foundation models.

The bill would also help copyright owners protect their copyrights, addressing widespread concerns from businesses and individuals about AI, by giving users more information to help them determine if they should consider taking action to protect copyrights.

Text of the AI Foundation Model Transparency Act is available here, with a one-pager on the bill here.

Rep. Don Beyer (D-VA) is Vice Chair of the bipartisan Congressional Artificial Intelligence Caucus and Vice Chair of the New Democrat Coalition’s AI Working Group. Beyer served for eight years on the House Committee on Science, Space, and Technology. He is currently attending George Mason University as a part time student completing coursework towards the pursuit of a master’s degree in machine learning, in part to help inform his work on AI in Congress.