The President’s Council of Advisors on Science and Technology voted unanimously in favor of the report Tuesday following a nearly hourlong public discussion of its contents and recommendations. The delivery of PCAST’s report will fulfill a requirement in Biden’s executive order on AI, which called for an exploration of the technology’s potential role in “research aimed at tackling major societal and global challenges.”

“Empowerment of human scientists” was the first goal presented by PCAST members, with a particular focus on how AI assistants should play a complementary role to human scientists, rather than replacing them altogether. The ability of AI tools to process “huge streams of data” should free up scientists “to focus on high-level directions,” the report argued, with a network of AI assistants deployed to take on “large, interdisciplinary, and/or decentralized projects.”

AI collaborations on basic and applied research should be supported across federal agencies, national laboratories, industry and academia, the report recommends. Laura H. Greene, a Florida State University physics professor and chief scientist at the National High Magnetic Field Laboratory, cited the National Science Foundation’s Materials Innovation Platforms as an example of AI-centered “data-sharing infrastructures” and “community building” that PCAST members envision. 

“We can see future projects that will include collaborators to develop next-generation quantum computing qubits, wholesale modeling, whole Earth foundation models” and an overall “handle on high-quality broad ranges of scientific databases across many disciplines,” Greene said.

The group also recommended that “innovative approaches” be explored on how AI assistance can be integrated into scientific workflows. Funding agencies should keep AI in mind when designing and organizing scientific projects, the report said.

The second set of recommendations from PCAST centered on the responsible and transparent use of AI, with those principles employed in all stages of the scientific research process. Funding agencies “should require responsible AI use plans from researchers that would assess potential AI-related risks,” the report states, matching the principles called out in the White House’s AI Bill of Rights and the National Institute of Standards and Technology’s AI Risk Management Framework.

Eric Horvitz, chief scientific officer at Microsoft, said PCAST’s emphasis on responsible AI use means putting forward “our best efforts to making sure these tools are used in the best ways possible and keeping an eye on possible downsides, whether the models are open source or not open source models. … We’re very optimistic about the wondrous, good things we can expect, but we have to sort of make sure we keep an eye on the rough edges.”

The potential for identifying those “rough edges” rests at least partially in the group’s third recommendation of having shared and open resources. PCAST makes its case in the report for an expansion of existing efforts to “broadly and equitably share basic AI resources.” There should be more secure access granted to federal datasets to aid critical research needs, the report noted, with the requisite protections and guardrails in place.

PCAST members included a specific callout for an expansion of NSF’s National Secure Data Service Demonstration project and the Census Bureau’s Federal Statistical Research Data Centers. The National Artificial Intelligence Research Resource should also be “fully funded,” given its potential as a “stepping-stone for even more ambitious ‘moonshot’ programs,” the report said.

AI-related work from the scientists who make up PCAST won’t stop after the report is edited and posted online next week. Bill Press, a computer science and integrative biology professor at the University of Texas at Austin, said it’s especially important now in this early developmental stage for scientists to test AI systems and learn to use them responsibly. 

“We’re dealing with tools that, at least right now, are ethically neutral,” Press said. “They’re not necessarily biased in the wrong direction. And so you can ask them to check these things. And unlike human people who write code, these tools don’t have pride of ownership. They’re just as happy to try to reveal biases that might have incurred as they are to create them. And that’s where the scientists are going to have to learn to use them properly.”

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Federal IT contractors developed a number key components of the IT infrastructure for the 2020 census, including the IT platforms used to collect data from households responding to the survey via the internet and telephone and for Nonresponse Followup Operation (NRFU) activities, according to a study of the implementation of the 2020 census by the U.S. The Government Accountability Office.

“The largest spending component of the 2020 census was contractual services worth approximately $5.9 billion, or nearly 44 percent of the total 2020 census cost as of September 2022,” the GAO study from March said.

“Contractor’s responsibilities included, among other things, evaluating the systems and infrastructure and acquiring the infrastructure (e.g., cloud or data center) to meet the Bureau’s needs for performance and ability to size the systems to operational scale,” the study added.

GAO undertook the study as part of a body of work to inform Congress about significant operational, management or technological issues that arose during the 2020 census. Previous assessments by the watchdog identified deficiencies in the bureau’s 2020 census cost and schedule estimation practices.

Additional IT contractor responsibilities for the census included integrating all of the tech systems and assisting with technical, performance, and scalability, as well as operational testing activities.

GAO made two recommendations to the Census Bureau’s parent agency the Department of Commerce, including that the Bureau use a more complete lesson learned process for cost and schedule during the 2030 census survey. The Department of Commerce agreed with GAO’s findings and recommendations.

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