WASHINGTON - Yesterday, the Committee on House Administration held a Full Committee Hearing titled, “The Congressional Research Service and the Future of AI-Enabled Policy Analysis.”

Witness:

  • Dr. Karen Donfried, Director of the Congressional Research Service

In case you missed it, here are the top takeaways: 
1. CRS Leveraging AI While Maintaining High Standards
  

Chairman Bryan Steil (WI-01): CRS' authorizing statute guarantees complete research independence to CRS while requiring the most effective and efficient service to Congress. You brought this up a little bit in your comments about AI. It's kind of balancing the speed of response, the accuracy, the depth, how AI assists, but also challenges this. You commented on the oversight, the accuracy of these AI models coming in. Can you provide some color as to how you and the team at CRS are thinking about leveraging this new technology while maintaining these standards and the speed at which you're able to deliver that product to members and staff?

Dr. Karen Donfried: It's a great question. And I talked about the five core values that CRS has, and those values are really our promise to you. So we are promising you what we send you will be nonpartisan, will be objective, will be authoritative, and we will do that research analysis in a confidential setting. No one is ever going to know what any of you asked CRS. And we also pledge that we'll get it to you in a timely fashion, because we know that if you give us a deadline, if we get it to you after that deadline, it's not going to be useful. So that's the promise. But yes, there is a tension across those values. So when we say we want to give you something authoritative, let's say you give us a very short time frame, but you want a fully cited memo. We might call the staffer who put in that request and say, okay, we want to help. We know the timeline you have. We may not be able to give you that memo, but what is the urgent thing that your boss needs to know? And we will do our best to get you that information. But the promise is in getting it to you in that time frame, whatever we give you is going to be accurate. So we always marry those values.
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2. Utilizing AI to Better Serve Constituents
 

Rep. Laurel Lee (FL-15): We are increasingly asked to respond to constituent questions in real time. And they involve, you know, oftentimes many complex issues, some of which you just touched on. It could be anything from a specific policy area to disaster assistance or help with a federal program or benefit, like veterans benefit or another federal benefit. And are there uses that you have found in CRS? You just touched on a couple with the data analytics and the graphics that you think would help our member offices in responding quickly and accurately to those constituent needs. I'm interested in the use cases you've identified and how you think those AI tools could be helping us serve our constituents better.

Dr. Karen Donfried: My advice to you today is go to CRS first. We are here to help you and your staff with that. We serve you in your legislative, in your oversight and in your representational duties. We do a lot with your constituent requests, whether it's helping your staff understand what grants might be available. One of our most popular reports is actually a liaison for who can your staff contact in your district or in the federal government to help constituents. And because I am not confident in the accuracy of AI models to produce analysis for you, I would prefer you come to us and we will do our very best.
Click the image or here to view Rep. Lee's Q&A. 
3. Building an LLM to Provide Bill Summaries

Rep. Stephanie Bice (OK-05): In your original statement, you mentioned that you had tested six LLM models to try to see if they were able to accurately summarize a bill, and that they were not successful. So I guess my first question to you would be, are we looking at potentially building a custom LLM that would be specific to CRS to be able to do bill summaries, or is that something that would be cost prohibitive?

Dr. Karen Donfried: So what we're looking at doing now is we're saying, okay, maybe AI can't help us produce a bill summary out of the gate. And that's why we then said, let's break down the bill summary workflow. What are all the discrete steps that our colleagues undertake to produce that bill summary. So one of the first steps is, right now we're prioritizing by saying, what are the bills that are going to the floor? We think an AI tool could be very helpful at doing that task and then telling the analyst, here are the bills that are going to go to the floor, focus on those.

Rep. Stephanie Bice (OK-05): So I guess what you're saying, though, is you're not looking at building an internal LLM that's specific to Congressional sort of language or bill language?

Dr. Karen Donfried: What we're trying to do right now is build that workflow tool that can allow us to produce more bill summaries. If we actually get funding, if the Library gets funding to build that AI platform and we can train an LLM on legislative data, it sets us up for then producing bill summaries. 
Click the image or here to view Rep. Bice's Q&A.