Harnessing Collective Intelligence to Map AI Debates

Hello and welcome everyone. This is ActInf.org stream number 7.1 on March 20th, 2024. We're here with Jamie Joyce and Speaker John Ash. We'll have a presentation, then some opening remarks, and a discussion. So thank you both for joining. Looking forward to this and go for it. Thank you so much. And we're so happy to be here. My name is Jamie. And, you know, as stated, I'm here with John Ash, who works with us at the Society Library. And today we're going to talk about how and why we can make sense of AI deliberations at the societal scale, using some proven collective intelligence methods that we've developed in order to help inform and hopefully improve decision making. And, you know, this methodology is something that we've taught to students at 32 universities through educational programming. And we've even been asked to extend this type of education to fact checkers around the world through the IFCN. But besides educational programming, one of the main things the Society Library is working on is an idol and enabling societal scale debate on complex issues by creating knowledge graphs. Now, I think we were asked to speak here today because of some recent AI generated deliberation graphs that we've been releasing on topics related to AI. For example, we got an advanced transcript of the debate between Connor Leahy and Beth Jasos. And then we created a number of debate maps about not only their arguments, but different arguments that could be made in the genre of topics that they were discussing. So you can go to our website to see those if you like. I'll show you what it looks like in actuality very shortly. But our work automating the creation of deliberation graphs stemmed from our work building them by hand. Essentially, we've been building linked knowledge graphs which map the arguments, claims and evidence from as many detectable points of view as we can find derived from about 12 different forms of media. And this includes anything from economic impact assessments to scholarly articles to podcasts and tweets. And we modeled the back and forth or pro con reasoning in these knowledge graphs. And again, we used to do this by hand. And when we did it by hand, analysts would have to spend 1000s of hours combing through archives and the internet to find relevant knowledge so that readers wouldn't have to. And of course, we don't just build deliberation graphs, we also enable this complex linked data from our knowledge graphs to be viewable through more flattened usable structures such as micro voting decision making models, which have been piloted at the city level, and something we call papers, which is an interactive interface that looks deceptively like a paper, but is unpackable across varying dimensions of argumentation, linguistic register, and you can even open sentences in the paper to see like a Wikipedia page of information about each and every sentence which contains a specific claim. But why would we do this? Well, once upon a time libraries existed because information was really scarce. That's not why they existed, but that's why they were useful. And it was helpful to have commonly held knowledge that people could go and collect. Now we have an overwhelming amount of information and it's a fine line to walk to both parse out relevant or duplicative information while also ensuring that you have the maximally context relevant information to include a particular issue. Essentially, it's like really easy to bias our understanding of an issue by omission of data. So we feel as though independent public serving institutions should exist like libraries, digital libraries, to ensure that people have the maximal context and the maximum amount of information available about complex issues without having to spend the thousands of hours finding it and analyzing it themselves. So just like the Library of Congress largely serves as the intelligence arm of Congress on policy matters because the intelligence community works…