“Federal administrative agencies — and that’s nearly all of them — get their name because they administer regulations spawned by laws. It takes people to make the countless decisions made in the every administrative state. Now some academics have looked into whether artificial intelligence might help agencies do our work. Stanford Law School law professor David Freeman Engstrom joined Federal Drive with Tom Temin with what they found.”
David Freeman Engstrom: “Sure, so we had our our teams look across 120 agencies, subagencies and and also, you know, full scale government departments and try to surface every possible use case they could. And they found about 160 of them. They found that 45% of those agency subagencies and departments were either experimenting with or had fully deployed machine learning tools of one kind or another. So you can really say that these tools now span the government, spans policy areas from law enforcement to education and everything in between…”
“At the Social Security Administration, some of the tools are involved in triage, it’s clustering together cases that are that are similar so that an administrative judge can proceed more quickly and equitably through those cases. So one of the problems that big, mass adjudicatory agencies like SSA have backlogs. It takes sometimes a veteran at the Board of Veterans Appeals wait years for a decision. Another problem at these agencies is inter judge disparities in decision-making. So though cases are randomly allocated to administrative judges, at SSA there are some judges that grant disability benefits 5% of the time, and some judges that grant them 95% of the time. And so something other than the merits of those cases must be driving those outcomes, so you can think of a triage tool that clusters together cases as a way to try to mitigate both of those problems…”
“So that middle part of the report has an entire chapter on really interesting use at the Food and Drug Administration, at the FDA. And one of the things the FDA has to do is monitor adverse drug events reports. So when someone has a reaction to a drug, it often gets reported into a system. Sometimes that’s voluntary on the part of the person who has experienced the problem. But they’re also mandatory reporters out there, and the result is that there’s a data set at FDA with just millions of these adverse event reports, and the agency has to parse those reports and make some sense of them. And so there is a natural language processing tool that FDA has piloted that tries to predict which of those reports, in which of those reports there might be an actual causal relationship between the drug and the adverse event…”
“The most obvious applications here would include chat bots, and a number of agencies are starting to pilot and even deploy chat bots, and so that’s that very direct point of intersection between state and citizen. And so, HUD has a chat bot that they have experimented with that would help people understand whether they might be entitled to housing benefits of some sort. I know the IRS is very interested in this…” Listen to the newscast here.
Source: Academics think agencies can make more administrative decisions with AI – March 11, 2020. Federal News Network.