While artificial intelligence is a multifaceted phenomenon, there are some shared perspectives among government officials. They’re pondering the workforce implications of AI and the emerging role of data as a driving force for improvement. They’re developing a small-steps approach to AI implementation, and they’re wrangling with potential ethical issues.

Here we’ll take a deep dive into some of these views.

Workforce impact

Despite concern among workers that AI could automate them right out of a job, federal leaders seem unified in their view that the purpose of artificial intelligence is to augment rather than displace the conventional workforce.

“We are not replacing people,” notes Captain Michael Kanaan, US Air Force Enterprise Lead for Artificial Intelligence and co-chair of the US Air Forces' Cross-Functional Team on Artificial Intelligence. “Any organization that starts with driving down costs or replacing people I think is creating an environment for a lot of perverse behaviors. This is about doing things better. This is about serving your customer better. This about taking care of your employees better.”

At all levels of federal government, people are mired down in mundane tasks and routine work that could be offloaded onto AI. A key promise of this technology is that it will free workers to tackle higher-level work.

“If you're already a knowledge worker and your engineering team comes with solutions that automate 20 percent of your tasks, chances are you can fill that 20 percent with other tasks that are more meaningful,” says Michael Karlin, Team Lead – Data Policy for the Canadian Department of National Defence.

Rather than taking jobs away from people, “I think ‘job transformation’ is more likely,” says Presidential Innovation Fellow Jeff Starr. “If my job is going to be changed by bringing machine learning methodology into my organization, the question is: How do I adapt to that?”

Across the boards, workers will need to have a better grasp of the ways in which data can be used to drive federal processes. They don’t have to know the nuts-and-bolts of how AI works, but they will need a higher level of data literacy all around. “I don't think we need 10,000 data scientists to descend on government,” says Presidential Innovation Fellow Justin Koufopoulos. “It's about having those critical thinking skills -- some analytical ability, some understanding of data.”

All our experts agreed that engagement is a key strategy here. Given widespread skepticism, neither the business nor IT can just foist AI on the workforce. There will need to be deep, meaningful conversations around the uses of AI and its potential for worker augmentation. Training will be integral to long-term success.

Rather than taking jobs away from people, "I think 'job transformation' is more likely," says Presidential Innovation Fellow Jeff Starr. "If my job is going to be changed by bringing machine learning methodology into my organization, the question is: How do I adapt to that?"

Data literacy as a core competency

Given these changes, many executives argue for the need for increased data literacy and coding skills throughout the workforce. As Congressman Will Hurd (R-TX) explains, “the only way we get to a point where this isn't a concern is if we train the future workforce. Our teachers have to prepare our kids for jobs that don't exist today. And that starts with exposing our kids to coding.”

As an employer, the federal government has a role to play here as well. Kanaan notes that “as a global enterprise, the Department of Defense has a long history of recognizing the critical and significance value that employees . . . who have secondary non-English language proficiencies.” However, he adds, none of those programs “measure or award individuals who have proficiency or fluency in any computer-based language. And I think we need to correct that hold-over from an earlier analog age.”

In many cases, it appears that these skills can be self-taught. As Alex Measure, an economist with the Bureau of Labor Statistics, explains, “I was fortunate . . . that we now have a bunch of free online resources like Coursera and EdX, and they had some excellent machine learning classes taught by leading experts available for free. And so that's how I as sort of acquired the initial skills.”

Dorothy Aronson, the National Science Foundation’s CIO, is working with the Federal CIO Council to determine if we can formalize this approach for bringing data literacy to non-IT audiences. As she explains, “we've offered this opportunity to people and they're just now jumping on the notion [that] we can use tools like Udacity to quickly up-skill people who are not IT necessarily . . . and give them the IT skills they need to, to use to leverage these intelligent tools.”

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