Solidifying American Leadership in AI

Solidifying American Leadership in AI

Mar 18, 2025
An artificial intelligence concept

Mathematica provided recommendations to the National Science Foundation (NSF) and the White House Office of Science and Technology Policy (OSTP) for solidifying the United States as the global leader in artificial intelligence.

“The rapid development and deployment of artificial intelligence (AI) presents enormous opportunities and risks,” wrote Mathematica. “Understanding where the gaps in the data are and how AI can strategically project data is key to success when using AI.”

Mathematica’s comments came in response to a request for information from NSF’s Networking and Information Technology Research and Development (NITRD) National Coordination Office (NCO) and OSTP to inform the development of an Artificial Intelligence Action Plan.

The Plan, directed by President Donald Trump’s January 23 executive order, will aim to advance America’s AI leadership and protect private sector AI innovation from burdensome requirements.

Specifically, Mathematica’s recommendations include:

  • Conduct proactive audits within government to enforce legislation that regulates the use of AI.
  • Define who is accountable and responsible for safeguards throughout the AI life cycle – work that must be interdisciplinary and involve public-private partnerships.
  • Implement sector-specific regulations to meet domain-specific AI use cases. To address concerns around enforcement, create a certification program similar to how electronic medical record software is regulated.
  • Use AI that is designed and evaluated for specific use cases to address major barriers to government services.

Mathematica’s response draws on its broad and deep expertise across disciplines and policy domains, as well as its use of AI across contexts – from public health to education to employment and labor – to inform public decision making. For example, Mathematica has explored the value of wastewater data to anticipate surges in public health emergencies; used machine-learning techniques to identify academically at-risk students; designed and trained AI solutions to predict unplanned hospital admissions; and predicted fatal opioid overdoses using geospatial analytics.

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