U.S. government agencies to use AI to cull and cut outdated regulations

In this news, we discuss the U.S. government agencies to use AI to cull and cut outdated regulations.

(This October 16 story restores lost words in the first paragraph to say tens of thousands of pages, not tens of pages; corrects the fourth paragraph to say 185,000 remaining pages, unpublished each year)

WASHINGTON (Reuters) – The White House Office of Management and Budget (OMB) said on Friday that federal agencies would use artificial intelligence to remove stale, outdated and inconsistent requirements on tens of thousands of pages of regulations government.

A 2019 pilot project used machine learning algorithms and natural language processing at the Department of Health and Social Services. The test revealed hundreds of technical errors and outdated requirements in agency regulations, including requests to submit documents by fax.

The OMB said all federal agencies are encouraged to update regulations using AI and several agencies have already agreed to do so.

Over the past four years, the number of pages of the Code of Federal Regulations has remained at approximately 185,000 pages.

White House OMB Director Russell Vought said the AI ​​effort would help agencies “update a regulatory code marked by decades of neglect and lack of reform.”

As part of this initiative, agencies will use AI technology and other software “to scan thousands and thousands of regulatory code pages to find places where the code can be updated, reconciled, and generally cleaned of technical errors, ”the White House said.

The participating agencies include the Department of Transportation, the Department of Agriculture, the Department of Labor and the Department of Home Affairs.

The General Service Administration will help agencies identify technology partners and facilitate contracts.

The Trump administration had made deregulation a key priority, while critics say the administration has failed to ensure adequate regulatory safeguards.

Reporting by David Shepardson; Editing by Aurora Ellis

Original © Thomson Reuters Corporation

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