The U.S. Department of Transportation (DOT) is testing the use of artificial intelligence to help draft federal transportation regulations, a move intended to speed up the traditionally slow rulemaking process. Agency demonstrations have shown AI tools, including advanced models, generating draft text that could serve as the basis for new rules ranging from vehicle safety standards to aviation and pipeline oversight. By automating much of the initial writing, officials hope to reduce times from months or years to a matter of days or weeks, accelerating how quickly regulations are proposed and revised.
Supporters within the government view AI as a way to handle the enormous workload involved in rulemaking, offering efficiencies in drafting language, organizing content, and synthesizing public comments. In internal presentations, agency attorneys have showcased how AI could manage large portions of regulatory text, freeing human staff to focus on refinement and higher-level judgment. The goal, these advocates say, is not to replace regulators but to streamline bureaucratic processes that are often bogged down by paperwork and procedural complexity.
However, the initiative has drawn significant concern and criticism from within DOT and among outside observers. Critics warn that AI tools can produce errors, “hallucinate” incorrect information, and lack the nuanced understanding required for complex safety and legal standards. In some internal discussions, staff have pointed out that draft regulations generated by AI often omit essential legal text or fail to capture important safety considerations, underscoring the risk of relying too heavily on automation for rules that affect public safety.
The debate over AI-assisted rulemaking reflects broader tensions in public policy — balancing efficiency and innovation with accountability and expertise. While proponents argue that AI could modernize how government develops policy, skeptics stress the need for careful human oversight, transparent processes, and robust quality controls to ensure that regulatory outcomes are accurate, equitable, and aligned with legal standards. As the discussion continues, DOT’s experiment could become a high-profile test case for whether AI belongs in core governance functions.
