Chairwoman Bice questions witnesses at Access to Legislative Data and Information hearing #3
House Administration: House Committee · July 21, 2026
A House subcommittee exchange asked how large language models can personalize legislative information while ensuring accuracy and public trust; an expert witness warned of model "hallucinations," cited a Stanford finding of 17–33% error rates, and recommended human-centered design, citation requirements, and user verification steps.
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