The 36-0 vote sent the bill (H.R. 6213), co-sponsored by Science Committee Chairman Frank Lucas, R-Okla., and ranking member Zoe Lofgren, D-Calif., to the House floor. The National Quantum Initiative, which was aimed at bolstering quantum research, expired Sept. 30. The reauthorization, the sponsors say, would build off the accomplishments of the 2018 law in an effort to ensure U.S. competitiveness against China and Russia.

“As China and Russia are actively making notable investments in quantum systems, we must maintain our momentum to secure our leadership position in this revolutionary field, and this bill does just that,” Lucas said in a statement after the markup.

Despite the 2018 legislation establishing the National Quantum Initiative as a 10-year program, its scientific activities were authorized for only five years, according to a June report from the National Quantum Initiative Advisory Committee. That panel recommended continued and expanded support for the program beyond its original sunset in 2028. 

At a previous markup of the bill in November, the committee adopted 19 amendments, all of which were approved by voice vote. 

Those amendments included one from Rep. Max Miller, R-Ohio, to add language directing agencies to consider the use of AI and machine learning in quantum science, engineering and technology, and how quantum might be used to advance AI and other emerging technologies.

The committee also agreed to adopt an amendment that directs the National Science and Technology Council’s Subcommittee on Quantum Information Science to “identify potential use cases with respect to which quantum computing could advance the missions of participating agencies, including through on-premises, cloud-based, hybrid, or networked approaches.” That amendment was offered by Reps. Deborah Ross, D-N.C., and Jay Obernolte, R-Calif.

Several amendments specifically expanded resources for the National Science Foundation, including two amendments for awards the agency would offer for quantum research. Those amendments were offered by Rep. Stephanie Bice (R-Okla.) and Reps. Andrea Salinas, D-Ore., and Jim Baird, R-Ind., respectively. Another amendment from Reps. Obernolte and Haley Stevens, D-Mich., would strengthen NSF’s quantum testbed activities.

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At the time of the young man’s death in 2014, police departments did not release use of force data — basic information needed by federal officials to determine how Black communities were being affected by law enforcement violence — and led her to spearhead the novel Police Data Initiative.

The effort started with 14 police departments committing to opening at least three datasets — their use of force dataset almost always being one — and 129 jurisdictions were on board by the end of the Obama administration. Police and citizen expectations of what transparency and accountability should look like, and what data should be open, had changed, Ross told FedScoop in an exclusive interview.

“But we didn’t create a mechanism for turning that data into action, so that’s why I’m back.” Ross said. “Because open data is necessary and not sufficient to drive the type of action that we need to create a more equitable society.”

Ross was a Presidential Innovation Fellow at the time.

Now the U.S. chief data scientist since November, Ross’ focus has been ensuring the data agencies are using and publishing are yielding more equitable outcomes for Americans. And that requires the “next generation of open data” as she sees it: disaggregated data.

Disaggregated data is separated into smaller units, often demographically, to answer questions like which populations are underserved by federal programs and policies and make course corrections narrowing service gaps. The process is time intensive and necessitates skilled data practitioners, including career federal officials upskilled in data science, Ross said.

President Biden stressed his commitment to equitable data from his first day in office with the immediate issuance of the Racial Equity Executive Order in support of underserved communities. When the White House’s Equitable Data Working Group (EDWG), created by the order, released its five recommendations in April for improving use, normalizing disaggregated data while protecting privacy topped the list.

“Now everybody is talking about disaggregating data,” Ross said. “It used to be — I’ve been in this field for 20 years — I always avoided that word because it was so jargoney, and nobody knew what we were talking about.”

Still agencies are a “mixed bag” when it comes to collecting disaggregated data and using it properly, she added.

Bright spots include the Federal Interagency Council on Statistical Policy recently releasing a searchable catalog of disaggregated datasets on Asian, Native Hawaiian and Pacific Islander populations, as well as agencies disaggregating grant data by location to ensure fairer distribution, Ross said.

The chief data scientist has spent the last 10 months building a small team within the White House Office of Science and Technology Policy that supports the Biden administration’s biggest priorities like the Bipartisan Infrastructure Law (BIL) with $1.2 billion behind it, Inflation Reduction Act and Customer Experience Executive Order reducing barriers to government benefits. For the first time, a dedicated team is applying disaggregated data to a president’s policy agenda.

“What I do is infuse equitable data into those priorities, so data isn’t a side thing,” Ross said. “It’s actually integrated into how we design programs and policies.”

For that reason it’s important the team be a diverse mix of genders, races, ethnicities and lived experiences, she added.

Ross finds the biggest obstacle to the team’s work is its hybrid nature; there aren’t as many in-person interagency meetings or civic tech innovation summits for sharing best practices since the pandemic began.

“The collaboration tools that we have are just mostly not compatible,” Ross said. “And so we end up making the most of what we can with a PowerPoint and a Zoom call, but that’s a far cry from being in the same room with a bunch of post-it notes and really doing some solid design thinking using the best available tools.”

Ross assists with the hiring of data practitioners within agencies like the Office of Personnel Management’s surge team for the White House’s BIL implementation, which requires hundreds of STEM-trained personnel to support investments.

The chief data scientist’s team is responsible for operationalizing some of the recommendations of the White House Equitable Data Working Group (EDWG), which is transitioning into the National Science and Technology Council’s Subcommittee on Equitable Data. Ross will co-chair that subcommittee.

In addition to disaggregating data, the team is uncovering underused data; improving agencies’ capacity for policy and program equity assessments; creating public data visualization tools; and soliciting feedback from state, local, tribal and territorial communities. Some communities surpass the federal government when it comes to disaggregating data, which is why OSTP recently issued requests for information (RFIs) on LGBTQI+ equity and equitable data engagement and accountability on behalf of the new subcommittee.

“We’re really serious about these RFIs because we need the wisdom from the field in order to be able to implement these Equitable Data Working Group recommendations, in the most useful way, inside the federal government,” Ross said.

Other EDWG recommendations fall to the Office of Management and Budget and U.S. Chief Statistician Karin Orvis, who’s currently modernizing the 25-year-old Statistical Policy Directive No. 15 (SPD 15) on race and ethnicity data standards. 

OMB recently released a plain-language recommendation to agencies for making the best use of existing race and ethnicity standards because the revised guidance isn’t expected until summer 2024. The recommendation includes practical flexibilities for disaggregating race and ethnicity data, approaches to data on more than one race, and advice on adding additional race categories to forms or surveys.

“I’ll just spoil that one,” Ross said. “You should not add some other race category to your forms or surveys because then it makes your data really unusable.”

OMB’s first listening session on the SPD 15 revision is slated for Sept. 15, 2022, and an RFI will be issued soon, Ross said.

The chief data scientist expects the update will have a ripple effect on how SLTT governments collect their own data with California already considering new race categories.

Ross will spend the rest of the year helping stand up the Working Group on Criminal Justice Statistics called for in May’s Policing and Criminal Justice Executive Order, harkening back to her work as a PIF, and ensuring her subcommittee hits the ground running.

“My priority for the rest of 2022 is to get these interagency collaborations going through the Subcommittee on Equitable Data,” Ross said. “That includes working on sexual orientation and gender identity data, infrastructure investment and equity assessments.”

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Within its RFI, the Office of Science and Technology Policy married biometrics for identification with technology for inferring emotion or intent and medicine’s understanding of the term as any biological-based data. MITRE would rather OSTP use the National Science and Technology Council‘s internationally accepted definition of biometrics limiting them to identity matters.

The U.S. lacks a comprehensive privacy law that would serve as the foundation for regulating AI, which has policy groups like the Open Technology Institute pressing the Biden administration for increased oversight and safeguards. OSTP wanted RFI respondents to examine biometrics through the lens of AI to inform the AI Bill of Rights government will use to protect people from problematic technologies but in doing so conflated three distinct concepts, which MITRE holds will lead to confusion.

“They kind of grouped multiple, different technologies into a single grouping, and those technologies all have different backgrounds, different operational issues and different policy considerations,” Duane Blackburn, science and technology policy lead at the Center for Data-Driven Policy, told FedScoop. “Grouping them together like that is going to really complicate the policy analysis and potentially leads to making improper decisions.”

MITRE’s second recommendation for OSTP is that it makes evidence- and science-based policy decisions because misconceptions about identity biometrics abound — the first being they’re not scientific in nature. Blackburn points to the decades of biometrics research, international standards, accreditation programs for examiners and university degrees.

The second misconception is about how face recognition technologies, specifically, are biased. Most people assume the bias is prejudicial for and against certain ethnic groups, and while that may be true for some algorithms, the assumption overlooks technical and operational bias, Blackburn said.

When face recognition technologies were first being developed 20 years ago, image lighting, pose angle and pixel numbers greatly impacted results — known as technical bias.

A face recognition algorithm trained for longer with more data performing more accurately than another is an example of operational bias, which impacts how the system works.

“There are not direct correlations between technical and operational biases and prejudicial bias, even though in a lot of policy analyses they’re treated as equivalent,” Blackburn said. “You can take a biometric algorithm with no differential performance technical bias and create systems with massive prejudicial bias.”

The opposite is also true, he added.

Lastly MITRE recommends OSTP ensure any policy decisions around biometrics are focused and nuanced, given the many biometrics that exist: fingerprint, face recognition, iris recognition and some aspects of DNA.

“You can’t really come up with a singular policy that’s going to be proper for all three or four of those modalities,” Blackburn said.

Using biometrics to unlock a phone is “significantly different” than law enforcement using it to identify a criminal, and decisions will need to be made about what data sharing is allowable under the AI Bill of Rights, he added.

An OSTP task force released a report on scientific integrity in early January reinforcing the need for technical accuracy when making policy decisions. Challenges aside, Blackburn said he remains optimistic OSTP is up to the task of crafting an AI Bill of Rights.

“How can we set up the policy so that it’s accurate from a technical, scientific-integrity perspective, while also meeting the objectives of the public that they represent,” Blackburn said. “It’s not easy, it takes a lot of time and effort, but OSTP and the federal agencies working on these issues have a lot of experience doing that.”

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GAO provided its first-ever priority recommendation letter to OSTP urging the office to work with agencies to establish common outcomes, joint strategies, and roles and responsibilities; address needs using their resources; and develop a way to monitor, evaluate and report results.

OSTP implements GAO’s recommendations at a faster rate than other offices — addressing 16 out of 17 recommendations across two fiscal 2017 GAO reports — but 11 recommendations remained open as of June. GAO established three open recommendations as priorities because of OSTP’s “critical role” convening agencies on National Science and Technology Council committees and subcommittees. 

“This mechanism provides a valuable opportunity for agencies to coordinate on implementing an administration’s research and development priorities and to address crosscutting science and technology issues, such as scientific integrity, public access to federally funded research results, reliability of research results, supply chains for critical materials, and others,” reads GAO’s letter. “Strengthening interagency coordination in these areas could help amplify the synergistic effects of related research conducted by different agencies, avoid unnecessary overlapping or duplicative research and development efforts, and facilitate the sharing of lessons learned or coordinating actions to address science and technology issues.”

GAO recommended OSTP, as co-chair of the NSTC Subcommittee on Open Science, coordinate with other co-chairs and participating agencies to implement collaboration practices in November 2019. OSTP initially disagreed with the recommendation but as late as May provided information on its efforts to work with other agencies to increase access to federally funded research results. GAO won’t close its recommendation until OSTP shows it’s attempted to address the practices identified though.

The second priority recommendation dates back to September 2018, when GAO recommended OSTP similarly implement collaboration practices as co-chair of the NSTC Subcommittee on Quantum Information Science. OSTP agreed with the recommendation in that case and took some steps to set goals in key areas as late as May, but GAO won’t close the recommendation until it’s fully addressed.

Lastly GAO recommended OSTP take steps to assess potentially critical minerals as a co-chair of the NSTC Subcommittee on Critical Minerals in September 2016. OSTP didn’t comment at the time but later stated it saw value in analyzing more minerals and non-minerals to inform policy decisions. In May, OSTP stated it was “actively exploring” broadening its focus beyond raw mineral and mineral challenges, but GAO won’t consider its recommendation implemented until there’s a plan for federal coordination addressing the data limitations hindering assessments of potentially critical minerals using a screening process the subcommittee develops.

In all three instances, GAO advised OSTP to consider whether participating agencies agreed to a decision-enforcement process, how leadership can be sustained and if there are documented collaboration agreements in place.

OSTP hasn’t responded to the letter.

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“While it is becoming well recognized that cloud computing can advance AI R&D, several technical and administrative challenges are currently limiting cloud adoption, ” the report from the Select Committee on Artificial Intelligence says.

For example, inconsistencies in the methods of accessing and using cloud computing create barriers to using the technology for AI research, the report says. It also cites limited access to education and training opportunities for cloud use.

The committee, part of the National Science and Technology Council (NSTC) at the Office of Science and Technology Policy, made four key recommendations to overcome these challenges. They revolve around pilot projects, education, identity management and interoperability among different cloud platforms.

The panel also suggested that pilot projects can help explore the challenges and advantages of using cloud computing in federally funded AI research, something that agencies like the Department of Defense’s Joint Artificial Intelligence Center, the Department of Transportation, and the Department of Veterans Affairs have already launched.

Agencies should then couple these projects with education and training opportunities for researchers to help “realize [cloud-based platforms’] full benefits for advancing AI frontiers,” the panel said.

Also in that timeline, the committee suggested that the NSTC’s Subcommittee on Machine Learning & Artificial Intelligence establish a task force to find best practices in identity management, such as token-based access, and single-sign-on strategies. The goal would be to respect data contributors while also allowing for discovery and research within relevant datasets.

“It is time to move beyond role-based access to data to new strategies that promote investigator access to data in a manner that provides fine-grained access control,” the report reads.

The select committee was formed in May 2018 with the mission to ensure U.S. leadership in artificial intelligence.

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In a new request for information, the White House’s National Science and Technology Council Subcommittee on Quantum Information Sciences (QIS) asked for agencies in the federal government, academics and the private sector to advise the administration on its quantum policy.

The RFI coincided with a panel of technology experts convened on White House grounds to discuss quantum innovation last Friday.

Questions in the RFI focus on how the U.S. government should be assisting the development of the next generation of computing. The RFI seeks public insight on how to help the private sector and ways federal agencies can partner with stakeholders to invigorate innovation.

“[W]hat roles can the U.S. Government play in enabling the innovation ecosystem around QIS-related technologies?” the RFI asks.

Another point addressed in the RFI is retaining the talent needed to build next-generation technologies like quantum computers. Immigration and retention of international students have been hot-button issues as the Trump administration seeks to change the immigration system.

The RFI comes as the White House and other federal agencies announce funding and policy initiatives to boost quantum development. Last week, the Department of Energy announced its intent to fund new Quantum Information Science Centers — the “flagship” for quantum development, according to the DOE.

The solicitation also references the National Quantum Initiative Act, a 2018 law that releases more than $1.2 billion in funding for quantum initiatives.

The White House will accept comments until July 29.

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