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AI: It's all about the fundamentals

Artificial intelligence projects -- from chatbots that deliver citizen services to purchasing-data analytics – are gaining traction in government, but success stories are mostly small and scattered.  A group of specialists from across the federal government recently gathered to discuss what's needed to bring AI into the IT mainstream. The discussion was on the record but not for individual attribution, and the quotes have been edited for length and clarity. Here's the group emphasized three fundamental points:

PARTICIPANTS

Gil Alterovitz

Presidential Innovation Fellow, Department of Veterans Affairs

Jim Chen

Professor of Cybersecurity, College of Information and Cyberspace, National Defense University

Shelby Hritz

Federal Sales Leader, Google Cloud

Robert Monroe Jr.

Computer Scientist, Defense Department

Jim Rahai

IT Specialist, Environmental Protection Agency

Donna Roy

Executive Director, Information Sharing and Services Office, Department of Homeland Security

Dave Shepherd

Program Manager, Department of Homeland Security

James St. Pierre

Deputy Director, Information Technology Laboratory, National Institute of Standards and Technology

Shannon Sullivan

Head of Federal, Google Cloud

Pamela Wise-Martinez

Chief Enterprise Architect, Energy Information Administration

1. Get your data in order

The ability to make sense of unstructured data is a key selling point for AI solutions, but the group stressed the limitations that come with sloppy datasets.

Part of the challenge is settling on standards, but multiple participants said the real key will be effective ontologies that connect and "translate" between multiple standards. "If we can really make our data smart, then the AI and the machine learning are just going to be phenomenal," one participant said.

However, government is already in catch-up mode on standards, another participant said, adding: "We can't get ahead of AI because it's already out. People are running it, and they're actually doing things." A third participant warned, "If we don't do those simple foundational things, I think we'll be creating chaos."

2. Fix the infrastructure

According to several participants, another key ingredient is getting agencies' IT infrastructures in order. Cloud-based services power many advanced analytics tools, so preparing data and workloads for the cloud is critical.

"There are powerful algorithms out there," one participant said, "but do you have the backing infrastructure to support it?"

Another divided agencies attitudes toward infrastructure readiness into three key segments. First, "you have people who are terrified of the cloud. They want to say they're doing something in the cloud, so they take their existing processes and put them in. But there's no point to it. It's probably more expensive than when they were running it on-premises."

The second segment has accepted cloud technology, and "you've just got to get your arms around your data," the executive said. "You need to start labeling data, and you need to have data that you can use." The third segment is "more of the cutting edge, and that's where we've really been successful. Health sciences, Department of Energy, NASA, some of the leading Defense Department organizations -- they're embracing [AI], and they're the ones that are really ready."

Other agencies are starting to follow suit, moving their systems to the cloud. "Somewhere around 27 percent of our IT systems are now either in the planning process or in the migration process," one executive said. "Our goal for this year is 35 percent. The more we focus on how to get to the cloud and the security and the training, the more we can understand that getting the data to the cloud is probably the point."

3. Build trust in the algorithms

The group also said the "black box" of how AI solutions reach their conclusions must be better understood before broad buy-in is possible.

"There are biases in your data, and there will be biases in your results," one executive said. "I'm not sure we're ready for maximum use of machine learning and AI until we persuade folks it's safe."

Agencies are looking for help in that area, another participant said, adding: "I think there are a lot of pockets of good work happening, but I don't know that at the federal CIO level, the strategy and the President's Management Agenda around data is going to meet the need of where we want to go with machine learning and AI."

A third participant said: "I really think the next landing spot is the privacy-impact testing of the algorithms themselves -- getting through that process of explaining what the algorithm is doing in a way that everyone, including the public, understands."

Note: FCW Editor-in-Chief Troy K. Schneider and 1105 Public Sector Media Group Chief Content Officer Anne A. Armstrong led the roundtable discussion. The Dec. 11 gathering was underwritten by Google Cloud, but the substance of the discussion and the recap on these pages are strictly editorial products. Neither Google Cloud nor any of the roundtable participants had input beyond their Dec. 11 comments.

A longer version of this article was originally posted on FCW, a sibling site to GCN.

About the Author

Troy K. Schneider is editor-in-chief of FCW and GCN.

Prior to joining 1105 Media in 2012, Schneider was the New America Foundation’s Director of Media & Technology, and before that was Managing Director for Electronic Publishing at the Atlantic Media Company. The founding editor of NationalJournal.com, Schneider also helped launch the political site PoliticsNow.com in the mid-1990s, and worked on the earliest online efforts of the Los Angeles Times and Newsday. He began his career in print journalism, and has written for a wide range of publications, including The New York Times, WashingtonPost.com, Slate, Politico, National Journal, Governing, and many of the other titles listed above.

Schneider is a graduate of Indiana University, where his emphases were journalism, business and religious studies.

Click here for previous articles by Schneider, or connect with him on Twitter: @troyschneider.


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