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Filing on Improving Government-Funded AI R&D

by Joshua New
by and

WASHINGTON—Today, the Center for Data Innovation filed comments about how to update the National AI R&D Strategic Plan with the Networking and Information Technology Research and Development (NITRD) program. The Center’s recommendations include:

  • Encourage Congress to address the federal government’s substantial underinvestment in basic and applied research that could generate large payoffs for AI;
  • Develop effective methods for machine-to-machine collaboration, ensuring different AI systems can easily and seamlessly interact and work with one another;
  • Encourage research into methods for achieving algorithmic accountability, such as algorithm confidence measures, impact assessments, and error analyses;
  • Expand research on both the cyber risks and opportunities of AI systems;
  • Explore how to best make shared data resources available for developing AI systems, such as through developing public datasets that are more diverse;
  • Launch a call for proposals to develop other training and testing datasets that could reduce bias through better public data;
  • Accelerate the development and adoption of effective and reliable benchmarks for AI systems;
  • Ensure the private sector continues to develop voluntary, consensus-based standards for new AI technology both domestically and internationally;
  • Create a competition that provides early-career monetary awards for AI researchers that are conditional on remaining in academia.

As Daniel Castro and Joshua New note in the comments, the ability of the United States to remain globally competitive in AI will depend in no small part on public R&D activities focused on accelerating the development and deployment of the technology. NITRD’s National AI R&D Strategic Plan is a valuable opportunity to maintain the United States’ competitive edge in the absence of a broader national AI strategy.

Read the comments here.

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