Software

5 Questions About Artificial Intelligence, Answered

As the federal government explores wider use of AI, agencies need to think about the potential pitfalls as well as the benefits.

Joel Snyder

Joel Snyder, Ph.D., is a senior IT consultant with 30 years of practice. An internationally recognized expert in the areas of security, messaging and networks, Dr. Snyder is a popular speaker and author and is known for his unbiased and comprehensive tests of security and networking products. His clients include major organizations on six continents.

The federal government continues to take a cautious look at the potential of artificial intelligence; most AI projects remain in the planning phases, according to a Government Accountability Office report. But many agencies think that AI has value. Here are answers to five common questions:

What are the Biggest Challenges to Incorporating AI?

AI has only become practical in the past few years, which makes it challenging to implement in government settings. Safety and security issues also pose hurdles. The White House has ordered agencies to address possible threats before implementing AI technology.

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Can AI be Fair and Impartial?

AI should “augment human work,” according to a White House order. Some agencies (including the Department of Defense) have published strategies for its use, but AI is only as good as the data fed into it. Ensure proper governance around any data used in machine learning, and beware of deeper biases that may be built into off-the-shelf tools.

Can AI Improve Citizen Services?

AI is a valuable tool. As the federal government looks to improve online experiences, agencies like the IRS have deployed chatbots to ease navigation through vast amounts of online resources.

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What’s a Good First Project?

Try to tackle a project that will make incremental improvements to a existing application in support of a citizen service. Overly ambitious projects drive up costs and increase risks of negative outcomes.

Should an Agency Build or Buy AI?

Leverage vendor expertise from the underlying AI technologies in off-the-shelf tools. Train AI tools on internal data sets whenever possible to create a real sense of ownership within the IT team.

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