DHS eyes ‘black belt’ program to create AI champions in all components | Federal News Network

Agencies are taking steps to build their staff’s skills in data and artificial intelligence tools.

The Department of Homeland Security, for example, is looking to create a “black belt” program to identify data and AI champions in multiple areas of expertise.

DHS Chief Technology Officer David Larrimore told ATARC’s AI and Data Summit that the agency is seeking to identify AI experts in areas such as fraud, biometrics and security. statistical modeling.

“What we’re trying to do around our black belt program is find out who the experts in the DHS organization are, and we want to create a black belt for that specific concept,” Larrimore said Nov. 17. .

Larrimore said the black belt program, which is in its early stages, will rely on its champions to drive AI and data analytics into all aspects of the agency.

“Ultimately, you become part of a larger community to help where people like you aren’t available – because there’s no way, but of the approximately 350 ongoing acquisition programs right now, everyone has someone who could be considered a black belt in AI,” he said. “Wouldn’t it be great if a black belt from [Customs and Border Protection] could go and spend six months in a FEMA program to help them get started? »

Larrimore said several components of DHS over the past few years have made “tremendous strides” in sharing data. He said the maturity of DHS data has been especially helpful in its immigration operations.

“Through this sharing of data, we’ve actually been able to treat and help hundreds of thousands of people, so the sharing of information, at the grassroots level, has been really important,” he said. declared.

Larrimore said DHS Chief Data Officer Mike Horton has served as an effective “traffic cop” for how the department shares data between its components.

“Not only does it try to understand how components can report data, but also how they can share information with each other in a structured way. We’re going to see over the next few years that radically mature political role, instructions to be able to support that,” Larrimore said.

Larrimore said DHS is looking at ways to use the data to improve its customer experience and reduce the burden on its customers.

But as the agency examines emerging technologies such as AI and machine learning, Larrimore said DHS will need to take a closer look at its policies regarding the use of public data and how DHS maximizes the use of the data it already collects.

“Especially on the management side, we have to constantly question the data we are looking at. And it’s only by working with components with data providers with data stewards that we really understand where the rubber meets the road… It’s only when those conversations happen, that everyone comes together. is agreed on the information that actually provides value,” he said.

William Streilein, technical director of the Department of Defense’s office of the director of digital and artificial intelligence, said the DOD, which aims to be “AI-ready” by 2025, is looking to improve overall literacy. data of its workforce.

Streilein said AI readiness will vary across DoDs and CDAO is taking steps to “tailor the size” of AI education to a wide range of positions.

“Someone who is in acquisition…at a high level, I think someone in that position needs to know that innovation is normal for the course. You may not be able to inspect and provide requirements on how the model works, but you need to know what it should do. [performance] metrics are absolutely critical,” he said.

Streilein said the DoD is also focusing on data interoperability standards across the department.

“The first priority is data. It’s good data, and so we’re taking that message through the DoD, to our partners, to vendors, even internationally to help people focus on the fact that you need good data before you being able to leverage analytics and AI to bring new insights and things like that,” he says.

Ben Joseph, the chief data officer for the Office of the Inspector General of the Postal Service, said the IG office is investing heavily in the data literacy of its workforce.

“I can actually build the best AI/ML model, but if I put it in the hands of my interviewer and he has a ton of questions, then we lose them. We want to make sure that [as] we are moving forward, we have a proactive approach to telling them what changes are coming. How do you actually interpret some of these patterns? And how do you actually put them in front of people and how do you use them? said Joseph.

Joseph said the USPS OIG is particularly focused on hiring new data-savvy recruits.

“We don’t want to invest a ton of time to turn everyone into data scientists. We need a mix of people like data analysts, data engineers, data scientists and people who can also communicate change and all that. It becomes an ideal analysis team for us,” he said.

The agency, he added, has the ability to produce advanced data analytics, but needs to ensure it has a workforce that can take full advantage of those capabilities.

“I can actually create the best model and all of these results, but if I have people who can’t really interpret a bar or pie chart, it’s not going anywhere. So I really have to educate my staff, investigators, auditors and everyone [on] how do you interpret the data,” Joseph said.

Udaya Patnaik, chief innovation strategist at the GSA’s Federal Acquisition Service, said agencies are looking for help on ways to start experimenting with AI and machine learning.

“We have agencies saying, ‘Help, we have petabytes of data here, that we’d like to be able to enable some of the automated machine learning tools as a service that we could deploy there. . We’re just afraid of what is happening on the other side. And that’s a legitimate fear,” Patnaik said.

Patnaik said FAS seeks to provide “safe spaces” such as test beds and sandboxes with limited data sets to test AI and machine learning before scaling projects further. beyond the pilot phase.

“It doesn’t have to be something scary or something that puts people in danger. It’s something we just try to remind people of,” Patnaik said. “At the same time, within the GSA, we’re looking at and thinking, ‘What can we do to enable this? What can we do to be able to create these experimentation zones, so that it doesn’t become intimidating for everyone? »