{"id":172180,"date":"2025-07-29T09:05:59","date_gmt":"2025-07-29T13:05:59","guid":{"rendered":"https:\/\/valorguardians.com\/blog\/?p=172180"},"modified":"2025-07-28T19:49:40","modified_gmt":"2025-07-28T23:49:40","slug":"doge-creates-ai-program-aimed-at-deleting-up-to-half-of-federal-regulations","status":"publish","type":"post","link":"https:\/\/www.azuse.cloud\/?p=172180","title":{"rendered":"DOGE creates AI program aimed at deleting up to half of federal regulations"},"content":{"rendered":"

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This program, “Doge AI Deregulation Decision Tool”, has already identified around 100,000 regulations not supported by law. DOGE anticipates up to $3.1 trillion of regulatory compliance for these regulations. Removing rules not backed by law may save up to $1.5 trillion annually. Doing this same job manually would require approximately 3.6 million man-hours. This AI program could cut the time down by 93%.<\/p>\n

From Forbes:<\/b><\/span><\/p>\n

The AI tool at the center of DOGE’s efforts is built to scan large volumes of regulatory text, then compare those rules against their enabling statutes and determine whether each section is mandatory or discretionary. In theory, if a rule goes beyond what Congress has required, or simply rephrases statutory language without adding any interpretive value, it could be marked for deletion. In practice, deregulating could get more complicated, as legal interpretation often lies in gray areas that AI algorithms may struggle to understand.<\/p>\n

DOGE’s analysis suggests that repealing 100,000 regulations through traditional means would require about 3.6 million man-hours of legal and policy work. This includes the time required to research the law, draft proposed repeal notices, review and respond to public comments, and finalize repeal actions. Using the AI tool, the presentation claims this workload could be reduced by 93 percent. Most of the reduction would come from automation of tasks such as generating initial legal drafts and analyzing public comment responses.<\/p>\n

The AI tool has already been deployed at a couple of agencies. At the Department of Housing and Urban Development, it was used to evaluate over 1,000 regulatory sections in under two weeks. A similar effort at the Consumer Financial Protection Bureau reportedly relied on the AI to draft 100 percent of the agency’s deregulation proposals. Whether these pilot programs can be replicated across the government remains to be seen. Some agency officials have voiced concerns about the accuracy of the tool’s legal interpretations. According to the Post, HUD staffers found that the AI misread the law in a number of cases.<\/p>\n

In the short term, the process of identifying and repealing rules will inevitably require human oversight. Regulations exist within a complex legal environment, and understanding their interactions with statutes, case law, and enforcement practices is not something that can yet be fully delegated to machines. However, DOGE has thus far framed the tool as an assistant that can reduce labor burdens and help focus expertise where it’s most needed, not as a substitute for human judgment.<\/p><\/blockquote>\n

Additional Reading:<\/strong><\/p>\n

Broughel, J. (2025, July 28). DOGE built an AI to delete half of federal regulations. Will it work? Forbes<\/em>. Link<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

This program, “Doge AI Deregulation Decision Tool”, has already identified around 100,000 regulations not supported by … DOGE creates AI program aimed at deleting up to half of federal regulations<\/span>Read more<\/a><\/p>\n","protected":false},"author":661,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[238,13],"tags":[],"class_list":["post-172180","post","type-post","status-publish","format-standard","hentry","category-government-incompetence","category-society"],"_links":{"self":[{"href":"https:\/\/www.azuse.cloud\/index.php?rest_route=\/wp\/v2\/posts\/172180","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.azuse.cloud\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.azuse.cloud\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.azuse.cloud\/index.php?rest_route=\/wp\/v2\/users\/661"}],"replies":[{"embeddable":true,"href":"https:\/\/www.azuse.cloud\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=172180"}],"version-history":[{"count":0,"href":"https:\/\/www.azuse.cloud\/index.php?rest_route=\/wp\/v2\/posts\/172180\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.azuse.cloud\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=172180"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.azuse.cloud\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=172180"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.azuse.cloud\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=172180"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}