The Unmistakable Impact of AI on Agencies

Featuring Nicholas Speece, Chief Federal Technologist at Snowflake.

When it comes to the culture of the machine learning and AI side, it's a couple of different perceptions. One: That everything's going to take my job. That's the first. We can go back to that, too. The bigger relevant thing there is, "What is this really going to do for my agency, and how can I work alongside these tools and how can I leverage those tools to be better at what I'm doing in my profession?" That starts at the bottom end.
At the top end, it's setting the right expectations and building the right architecture so that folks don't feel cornered or that the tool is more enabled than they are. I'll give you an example there. We've seen a lot of times where a machine learning algorithm is given access to auditing data or billing data that even their own auditors at an organization were not given access to. That's kind of a backwards way of doing it and not how we want to do business in the future.
The robot economy is not going to be taking the jobs from our experts. It's going to be informing our experts in better ways.
I go back to the medical analogy I made earlier. We don't want an AI assigning prescriptions to a patient. What we really want is for the AI to say, "I've seen this combination of factors in a multitude of other folks that met the same criteria this person does and there might be a risk to giving this same prescription out and here are some other recommendations of things that worked for those patients." That's the sort of informing construct that we really want AI and ML to take on.