This week we’re chatting with David Wasserman of Alta Planning and Mike Flaxman of Heavy.AI about generative artificial intelligence. We chat about what generative AI is and how it is trained, and some of the ways it could be used or misused in a planning and transportation context.
To hear a full unedited transcript, click here, but there's also an edited and cleaned-up version below:
Jeff Wood: I’m wondering if this much data can be overwhelming. There are a lot of places right now that may get money there from the Inflation Reduction Act for all kinds of things from broadband to transportation money, but only have like one planner. And so I’m wondering if like this actually might create an overwhelming avalanche of information that might be just too much.
David Wasserman: That’s very real. To some extent, our ability to transform data into insight is increasingly becoming a bottleneck. Tools like this can help with that. But at the end of the day, if you’re one planner, is it reasonable to expect them to be able to, like how much, how much people’s expectations change not only of their staff, public sector staff, but also generally of each other? And I don’t know what the answer to that is either.
Mike Flaxman: I do see it in general as leveling the playing field at bit. I mean it’s long been the case that communities that are good at grant writing get disproportional resources, right?
Jeff Wood: Right.
Mike Flaxman: So this helps a bit, but one planner or one grant writer can get a little further with some of this technology, but how this affects the overall mechanisms by which grant programs are run and administered is a different question.
David Wasserman: And they’re using it to write grants based on some of the surveys that we’ve seen and conversations I’ve had with MPO directors. It’s happening. Wow.
Mike Flaxman: The other slightly nefarious angle is the elephant in the room: There is absolutely a potential for this technology to overwhelm public comment systems. And there’s already some evidence of kind of systematic attempts to do so. This technology can be used to create fake personal profiles to create fake public comments and wage bot attacks.
So, you know, all of these things are not exactly news because we’ve already been dealing with them, but the level to which they’re occurring, there’s a significant threat to at least naive public comment systems that assume that only humans can write. And who knows soon there will be video testimonials, too. So the ground has changed in terms of public comment and maybe that’s the flip side of what we’re talking about, the ability these models to consume and synthesize public comment in multiple forms. The flip side of that is that they can be used disingenuously to generate large amounts of verbiage, large amounts of public comment.
And so we need to be on, you know, on the watch for that. That’s something that planners have had to deal with a little bit, but we’ll have to deal with a lot more.
David Wasserman: We require them to be more sophisticated about how we conduct online engagement too. Alta Planning did actually have a bot attack on one of its public engagement surveys. Now we have a very sophisticated web team, so we were able to sort through that. But you know, not everybody who puts out a SurveyMonkey survey is going to be able to figure out what’s real and what’s not. So I, I think there are questions of capacity that I know APA is thinking about a lot. APA now has multiple memos and reports, foresight reports on the topic of artificial intelligence because there is this recognition that we would be remiss if we ignored some of both the capabilities that the technology offers, but also some of the risks and threats.
