Ai in Government - my musings

Ai in Government?

The use of artificial intelligence (AI) in the private sector has dramatically increased. In the public sector, however, AI usage has moved at a much slower pace even though it has the power to help governments deliver on their mission and significantly improve the way they provide services to citizens.

What’s the status of AI in government?

There is no single answer here – after all different governments have different approaches to AI. Some governments are a bit more advanced than others in terms of regulation and adoption, and how they think about AI and how they're using it. So, there's a wide spectrum. Let’s be honest - things in industry are moving so quickly that even without the fast pace, it's traditionally been hard for governments to keep up with technology.

Of course, things have really exploded since November of 2022, in terms of hype and potential. But I think there's a lot of work to do both in the private sector and government.

What are the opportunities for AI?

AI is based on learning patterns from data. Anywhere there are repetitive patterns, those are areas in which you would apply AI. When you think of it from that perspective, there are tons and tons of applications both in the private sector and government. And arguably, many processes in government are repetitive, as they are in the private industry.

There are kind of two branches of AI, and the techniques are pretty much the same or very similar. One is predictive AI and the other one is generative AI. Predictive AI basically consists of techniques that make predictions. They don't necessarily make predictions about the future, they just predict labels or classifications for things.

Generative AI generates content. When we see some of the more recent AI advances, generating fancy images and interesting text and so on and so forth, that's the generative part. The predictive part would do things like just classify tasks and label things.

I think most of the applications of AI in government will probably be on the detection side, in the predictive area. I can see great opportunities in Fraud and Error as I can for Benefits or Healthcare. The most exciting area will likely be in and around pointing people to information or helping citizens get what they need more quickly.

There's a lot of information within different agencies. Citizens need access to that information, but it's often hard for them to get it. The same often holds true for government employees. And for them, the challenge is understanding what something means or where the information is. This makes search an important application, but it could go further into predictive search or even turning things on it’s head and using data to serve information tailored to an individual well before they use the search function.

This is cutting-edge in the sense that some of these techniques require quite a bit of additional work to put the right safeguards and guardrails in place so that a chatbot doesn't go off the rails and start talking about something that is irrelevant or giving false information.

What are some of the barriers for government departments in leveraging AI?

Education: Understanding AI and gaining the skills to leverage and use AI.

There are multiple challenges when it comes to making AI a reality in a government or a government agency. One of them is education, and making sure that stakeholders understand what AI is, what it does, when it succeeds, when it fails, what the risks are—and what it takes to build AI, deploy it successfully and monitor it so that it does what it's supposed to do.

Data to build AI systems

The second challenge is data. Access to data is sometimes in silos, and so you need a data strategy. You need a cohesive view of how the data may be used with all the protections to make sure privacy is preserved.

Skills to build AI

The skills needed to build AI: This is another potential limitation. Sometimes governments lag a little bit behind in hiring the right talent, but this is key. They need to hire people with expertise, those with some experience—and ideally talent that has significant experience in building AI teams and ecosystems.

Organizational structure

There is the challenge of organizational structure. How are things set up? Who owns the data? As I said, data is often in silos. Maybe one agency has data that's useful, but doesn't share it with another agency, even if the data is public. Sometimes organizational changes within government can have a big impact on how that's done.

Computing infrastructure

One thing that’s happened in the private sector over the last several years has been the move to the cloud, where you can efficiently run processes on potentially thousands of computers just off of a laptop. You're essentially renting to compute. That makes it very easy to scale when you need a lot of compute power. You use it immediately and pay only for what you use. When you don't need it, you reduce what you use and only pay for what you're using.

Summary thoughts

I recently saw a quote that resonated with me:

“One mistake people often make with AI is they go in one of two extremes. They either think it's magic and can do anything, or that it's too complicated and that the organization isn’t yet ready for it.”

One of the challenges with government is that the data is often very confusing. When people need any kind of service, there are a lot of sources, they often don't know which agency they need to go to—and when they do find it, the information is written in a language that's hard to understand, even for the most educated. So I think technology that enables AI to be the interface with citizens, which has advanced pretty significantly in the last couple of years, could be very useful in helping citizens better understand that information and get what they need more quickly. If it’s done faster and better, governments can provide better access.