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How Ideas Spread—and Why You Believe Them
22 Plays21 hours ago
How do beliefs form—and why do they change? Associate professor Josh Introne explores “belief landscapes,” using AI and data to map how populations think, shift, and polarize. From misinformation to cultural bias in AI, this episode dives into the forces shaping collective intelligence and how we can build more resilient information systems.
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Transcript
Introduction to Infoversity and Guest
00:00:08
Speaker
Hello, I'm Jeff Hemsley, and this is another episode of Infoversity from the iSchool at Syracuse University. Today, I'm joined by my colleague, Josh Introne. He's an associate professor here at the iSchool at Syracuse, and he's the director of the C4 Lab and CCDS.
Understanding Belief Landscapes
00:00:27
Speaker
Josh studies how social technologies shape collective intelligence and belief formation, including large-scale work like Belief Landscapes, which is a computational social science project that he's going to tell us more about in a bit.
00:00:41
Speaker
In fact, let's just start right there. When you talk about belief landscapes, what does that mean and how are you studying that? Yeah. Well, so belief landscape is a metaphor, right? Of course, um for trying to understand how populations of people update their beliefs over time. So um let me give you a little bit of history on that, and then maybe I'll i'll walk you gently into belief landscapes.
00:01:11
Speaker
So... um We all know that, you know, or or many of us here in the high school have ah focused on misinformation. And ah misinformation is really about the proliferation of information that is that is not correct, right? People are putting it out there. and But I've always been wondering, I've always been interested in in in trying to figure out why people believe in misinformation.
00:01:38
Speaker
And it struck me that maybe the problem isn't the information itself, but it's really the ah latent beliefs that people hold that lead them to buy into that misinformation.
Modeling and Predicting Belief Shifts
00:01:54
Speaker
So I think about 10 years or so ago, I started um looking carefully at beliefs, trying to model beliefs.
00:02:05
Speaker
And um the metaphor or model that I came up with is that ah beliefs could be organized as if they were on a landscape and the population is moving over that landscape as they change their beliefs. Okay. And you can kind of another another metaphor is to think about um beliefs as being a current in a river.
00:02:35
Speaker
right Wait, can you give us a real world example in the, like in today's landscape? Yeah. so um you can kind of think about ah conservatives and liberals as being different positions on that landscape. Of course, there's not just conservative and liberal, right? There are lots and lots of little clusters of beliefs here and there. And so if you imagine instead of beliefs moving between people, you have people moving between beliefs, you can begin to think about how populations move across that landscape of beliefs, right?
00:03:14
Speaker
And so um i went... into this with that model in the back of my head and ah through a lot of natural language processing and some really fancy data analytic techniques, I was able to show that this model works pretty well.
00:03:38
Speaker
And in fact, it works so well that once you can locate people on the belief landscape, you can predict with a fair degree of accuracy where their beliefs are likely to go in the future.
00:03:52
Speaker
So you can't necessarily do this with 100% accuracy for an individual. But when you start to look at populations, you can say, oh this group of people who hold these sorts of beliefs will go here in the next year. half a year in the next year.
00:04:10
Speaker
And it's surprising how accurate that is. So the landscape metaphor, which is, know, you know maybe helps to think about as you can imagine a rough topographic landscape of beliefs. And we'll imagine there are stable positions on this landscape, and those are the valleys of the landscape.
00:04:35
Speaker
And the population of people moving across that landscape are governed to some degree by gravity. And so they flow into these low regions and they tend to stabilize there. They tend not to move back out of those.
00:04:52
Speaker
Now, of course, people are you know unique. People are all over the place. And it turns out that there are some people that are pretty stable and there are some people that are just wild and they're moving all over the place.
00:05:05
Speaker
um Most people are fairly stable. um It's really a ah small periphery of people that move all over the place. So in my mind, what I what i kind of envision me is like a map with different cities, and each city represents a belief space, except some cities or some belief spaces have more gravity or are more attractive to people.
00:05:31
Speaker
That's right. Is that kind of the ballpark? That's exactly the ball right. That's right. So now to mix metaphors a little bit. Yeah. um If you take that back to say the stream metaphor or the current metaphor, there's actually a lot of movement in this landscape. People are motion all the time. But in the same way, you don't really see those streams directly until you drop something in the water and you begin to see how those particles move like leaves on a stream. That's kind of how I do my work, right? As I model where people are and I look at their collective movements. And so you can begin to identify things like converging regions, little whirlpools, areas where there's a lot of turbulence.
00:06:23
Speaker
And then you can look at how that changes over time. You can look at the emergence of turbulent areas or new strong whirlpools emerging. So this becomes a really powerful way to look at things like polarization and belief dynamics.
00:06:40
Speaker
Yeah, so as you talk, I think about some sort of per perturbation, which probably equates to events that happen in the real world that can shift things. That's right. And so part of what I'm doing in my current project is trying to measure the –
00:07:00
Speaker
fragility of an existing belief landscape such that when something happens, will we see a massive movement or a shift in the beliefs of the population? So you might not be able to predict COVID, right? But you might be able to say that, oh, we're poised to do some pretty interesting things when a pandemic comes along.
00:07:25
Speaker
So, the project that I'm looking at is looking for the, ah you know, can we can we predict um where social unrest is likely to, you know, arise in the wake of certain kinds of events.
00:07:43
Speaker
Okay. So in layman's terms, How do you model this? How do you study this? Like what kind of data might you use? What what does that look like? Right.
Analyzing Belief Distributions through Text Data
00:07:54
Speaker
So the data that I use is any text data.
00:07:59
Speaker
So I look at anywhere where people might be voicing some kinds of beliefs. So this could be news media, social media, of course.
00:08:11
Speaker
um and But there are flavors of social media, right? I'm not going to be looking at YouTube videos typically because it's hard to lift beliefs out of that. that But any text data works for me. Could you use TikTok data it was transcribed? If it was transcribed, yeah. Yeah.
00:08:31
Speaker
um One of the things that's interesting is, you know, if you look at things like local news organizations, you can do a pretty good job trying to understand the beliefs of local populations by looking at just those news organizations.
00:08:48
Speaker
And so you can begin to understand how beliefs are distributed geographically as well. Mm-hmm. Okay, that's pretty cool. So now I know another space that you're looking at is AI and culture, and you have some PhD students working in that space. um Can you tell us about that?
00:09:09
Speaker
Yeah, sure. um So i think, ah i don't know if Google coined this phrase, but think There's this idea of value pluralism in ai right now, which is that people have lots of different values. What's right, what's wrong is really dependent on context and culture. There is no one-size-fits-all set of values.
00:09:43
Speaker
But most of the AI models that are generated are generated from you know a handful of tech companies out there. And they plug into those tech companies a set of values which might not fit everyone, right? And so here in you know the US, s we've got ah you know models that are generated in Silicon Valley and they're channeling usually liberal Western progressive values.
00:10:14
Speaker
And those don't even work that well in the US, right? there are Lots and lots of people with different ideas. and so you have to ask the question, what is you know is are these kinds of models excluding large portions of the population because they're coming into things with these baked-in values that don't really match the end users?
00:10:35
Speaker
So what we're trying to do, or or my my two PhD students are both looking at aspects of this. One of them is looking at um values around, not values is a little narrow actually. She's looking at conceptions of gender across different cultures, trying to understand how different AI models interpret and promote different views of gender.
00:11:03
Speaker
um And then my other student is, this Jin Fen, is looking at how you can actually adapt AI models to do something that recognizes the validity of the values of the end user.
00:11:24
Speaker
Right. And so the whole point here, now there's a ah larger question. ah You might have heard of the the alignment problem in AI, which is how do you make sure that the AI is aligned with human values?
AI and Human Values in Various Cultures
00:11:40
Speaker
right But the question then that begs the question, what values are we talking about here? Because there is no one size fits all set of values. So we're trying to approach that problem by saying, well, you know since we can't say there's one size fits all, one set of values, how do we align AI with different populations of users that are coming from different cultures, different backgrounds?
00:12:08
Speaker
Okay, so from what you're saying, it kind of sounds like you couldn't make AI to be culturally neutral, or could you? It seems really hard to do that as soon as you talk about anything that has, you know, if you want to ask AI for advice, at that point, it's really hard to be neutral, right?
00:12:30
Speaker
From a collectivist culture, you know, in the East, you might want to ask, should I move out of my house? Right. and from a Western perspective, yeah move out of your house, get a job, do something. From an Eastern perspective, spending time taking care of your parents, your aging parents is really a laudable thing to do.
00:12:52
Speaker
And so these values are going to inform how the AI responds.
00:12:59
Speaker
So let's briefly go back to belief landscapes. Yeah. So how is that an iSchool topic and how do you engage iSchool students in that kind of work?
00:13:12
Speaker
Yeah, it it intersects with the iSchool in a couple of interesting ways. um From a method standpoint, I am doing things that iSchoolers do. We're processing language, we're looking at social media, we're doing computational social science, and we're trying to understand the implications there.
00:13:35
Speaker
It's not necessarily that we're looking at how social media works influences beliefs yet. However, you know, I started with misinformation. the question then becomes, can we identify places that are especially susceptible to misinformation and what kinds of misinformation are they susceptible to?
00:13:57
Speaker
too. So, you know, maybe we're never going to stem the tide of misinformation. It's been around forever. It's becoming more and more powerful. I don't see how we're ever going to get rid of deep fakes, right? That, that that you know, train has left the station. But maybe we can do something to identify susceptible regions of a belief space and and based on this analysis, maybe we could say, oh, this is a kind of belief space that is highly susceptible to misinformation. This might be, ah you know, in another situation or another set of beliefs might be a highly resilient set of beliefs. It's not like we're trying to change anyone's mind about anything. But maybe there are ways we can introduce new sorts of dynamic processes which lead people to be more resilient in the face of bad information. So that's one place where this, um you know, this sort of intersects really strongly with the iSchool.
00:15:05
Speaker
and with information sciences in general. Another, my my goal here to some degree, um we do do things like try to understand polarization, right?
00:15:20
Speaker
ah Maybe that's political science. Maybe it's information science. Maybe it's communications. But it's somewhere in all of those places. I think there are a lot of different ways to characterize the dynamics of belief that go on in a population that are not just polarization. Polarization is pretty simple. It's based on the idea of a two-pole axis of beliefs, but that's not how beliefs actually work. There's fracturing, right? There are beliefs that are converging on some dimensions and moving apart on others. There are beliefs that are frozen. There are beliefs that are mixing.
00:15:58
Speaker
So using the belief landscape analysis, I develop a much richer vocabulary to describe these dynamics. And from there, we can do a lot of other things to think about our information ecosystem and how it intersects.
00:16:14
Speaker
Yeah, I like how you talk about how your work is actually intersecting with a lot of other fields. And that's actually kind of a key thing about high schools, right? We kind of sit in between a lot of other spaces. So students that are interested in multiple kinds of things can find work like this interesting from a lot of different perspectives. That's right. It's highly interdisciplinary. And we're always moving into this theoretical literature, that theoretical literature, and pulling down new new techniques.
00:16:46
Speaker
So tell us about CCDS. What is CCDS? um What's your role there? And again, how does that fit into the iSchooling? How do students get involved? Yeah, so this year i' am the director of the Center for Computational and Data Sciences.
00:17:02
Speaker
ah CCDS has been around for quite a while. um But I think one of the things that I'm hoping to do with CCDS um is actually...
00:17:17
Speaker
turn it into something more of a a connector, right? That connects ah students in the iSchool and faculty in the iSchool and maybe people across the campus with others in ah in the area, like in central New York, right? Industry leaders um who all share an interest in doing some sort of data analytics, right?
00:17:48
Speaker
So we have this ah really dense concentration of people with expertise in data analytics and AI. And in the iSchool, we're really interested in real world problems, right?
00:18:04
Speaker
And so there's a beautiful marriage there between what's going on in the iSchool, what's going on in Central New York at large, what's going on across the campus with people processing data,
00:18:18
Speaker
And so I envision CCDS to be a nexus of sorts where all of those people could come
CCDS's Role in Data Analytics
00:18:24
Speaker
together. So through that, we can provide students with real world opportunities to get involved in data analytics projects, maybe help them extend their networks. right And then we can also centralize data repositories and perform services. for ah others you know out out in the world or on campus.
00:18:49
Speaker
um And hopefully one day, we could establish new sorts of research relationships, engagements between faculty here in the I School and people trying to solve real world problems with AI automation, data analytics, forecasting.
00:19:06
Speaker
So now you also are in the process of trying to build some industry connections. Yeah. Are those things you can talk about? Yeah, sure. um So we have a contest coming up soon. This was a a connection we made through made through ah an iSchool student, a current iSchool student. um There's a ah company in the...
00:19:31
Speaker
Midwest called Professional Agricultural Marketing. um And they're running a contest here in the iSchool for iSchool students to participate in um addressing a couple of the challenges that they're facing with the deployment of AI and forecasting predictive analytics. So they reached out to us and we said we'd love to do that. And so we have I think a total of 16 student teams starting right now.
00:20:06
Speaker
um We have a kickoff meeting this Friday and they're competing for prizes, but also they're gaining a lot of experience with real world.
00:20:17
Speaker
problems um And then I've been reaching out MACNE, which is the Manufacturers Association of Central New York.
00:20:28
Speaker
um And i think we are going to be running a webinar in early April. And I'm working to try to build bridges into into industry more generally. um There's a center at SU called the Case Center, um and they've been helpful in trying to provide news contacts. So it's work in progress. I feel like we're a little startup here in the iSchool right now. Yeah. Well, and that's kind of exciting. And in fact, the iSchool has a long history of being entrepreneurial and starting up things and creating connections.
00:21:04
Speaker
So it kind of fits right in. So you also have a lab called the C4 Lab. Tell us about that.
C4 Lab and Student Collaboration
00:21:12
Speaker
Well, the C4 Lab, I started when I first... i'm I have a PhD in computer science, and i I did all my work in a lab environment. and I like having lots of students working together. i think there's a lot of synergies there.
00:21:29
Speaker
And so when I got to the iSchool, I wanted to replicate that environment. So I started the C4 lab. C4 stands, and I don't even remember the ordering, but it's ah communication, computation, cognition, complexity. That's C4.
00:21:48
Speaker
And those are all elements of you know of my interest in my work. um And so I have, you know, on any given day, anywhere between 15 and 30 students working on different projects in the C4 lab. a lot of students come to me and they say, you know, Professor Introne, I'd love to work on a research project. I say,
00:22:14
Speaker
I don't have any money. And they say, that's okay. I really want some experience here. i really like the idea of learning outside of a traditional course. And so I find spaces and projects. Students come in and they drift out again. you know, students are busy. you know, quite a few of them get some good experience and they stick around for a while. So within the Belief Landscape Project, I probably have...
00:22:41
Speaker
five or six students that are working on different aspects of the project. I have students working on a another project, a narrative project. I have students working on another project, which is building an app.
00:22:56
Speaker
um And so it's a really rich environment and I get a lot of a lot of good collaboration and synergy there. Okay, I'm going to switch to teaching. I want you to tell me um what classes you're currently teaching.
00:23:11
Speaker
And, you know, in education right now, there's tension around students using AI to learn things and using it in class and using it. What's your philosophy there? and And how do you see students applying this in useful ways in your context?
00:23:31
Speaker
Yeah, so right now I'm teaching teaching a graduate course in applied machine learning. um I'm only teaching one course this semester because my project is pretty demanding, um the funded belief landscape work.
00:23:49
Speaker
ah But I've been teaching this class for a few years now. It's evolved a lot, especially over the last few years as AI, as you might imagine, has is become really, really prevalent. People are using AI all the time.
00:24:10
Speaker
um As a programmer, i have about four decades of experience programming, and I use AI all the time to do my programming.
AI in Programming and Critical Engagement
00:24:21
Speaker
I'm a ah good coder, but it's just so much faster. right And I think that moving forward, we're going to see students using ai more and more and more, and there's really...
00:24:37
Speaker
No way to say, don't, don't, don't do that. I think it's possible to use AI and use it wisely to do really powerful things. But that's the trick, right? Is using it wisely. Because you can say to an AI, do X for me.
00:24:55
Speaker
And if X is too large or you're not paying attention, you have no idea what you're getting, right? And so what I'm doing with my applied machine learning class is focusing really on core theory to try to make sure students are understanding what's going on. But then within the coding part of the class,
00:25:20
Speaker
I try to scaffold interactions with AI so that people, students become very effective technical project managers. And that's one of the things that I think ah requires a lot of sophistication and is not well addressed in computational classes. It's kind of software engineering, but it's also a lot of what you call computational thinking, thinking about how you break code down into modules, how you make sure that those modules are performing correctly, and how you adapt the code as you move forward. That's the kind of thing I'm focusing on right now in class. So rather than
00:26:09
Speaker
sort of put my head in the sand. um i think ah grappling with AI directly and trying to teach students the new skills that they need in order to make sure they're using AI wisely in the course of doing technical work, is um that's that's my philosophy. So what if there's one thing you want to make sure your students leave with, with respect to AI, what is it?
00:26:38
Speaker
Well, I think some some you know condensation of what I just said. um
00:26:49
Speaker
Even though you can use AI to solve a big problem right now, it's important to keep looking at your problems critically and understanding how they're structured and using the AI as a targeted solution within that structure. so My hope is to make sure that AI doesn't lead students to disengage critically with the technical material that they're grappling with,
00:27:22
Speaker
So i hope to preserve that critical thought that goes into these sorts of technical disciplines, even though they have ai available to them, if that makes sense.
00:27:38
Speaker
Yeah. So another thing that I think a lot of students don't realize is there's many times we learn as much from them as they are from us.
00:27:49
Speaker
Talk about that. what What are you learning right now from your students? And I know you work with undergraduates and master's students and PhD students.
00:28:00
Speaker
So...
00:28:04
Speaker
it's it's It's hard to put it in ah in a nutshell because I learned different things from different students. Sure. Right? um A lot of ah what I learn is um how to articulate...
00:28:22
Speaker
ah things that I have just sort of absorbed over time, but I never really turned into some sort of lesson. I have a lot of intuitive approaches for dealing with things. And I'll say to my students, you know, when I'm first starting out with a class, I'll say, do this.
00:28:44
Speaker
It's obvious, right? And the students tell me, no, it's not obvious. And so that helps me articulate exactly what it is that you need to do in order to do X, right? Whatever may be. Kind of sounds like good prompting. Yeah, it's excellent prompting. And so have sort of step in it first and figure out, oh, I just said something that completely uninterpretable. And then, you know, once I get the feedback, I'm able to fix that. But then, you know, the other side, I'm working with, you know, PhD and master's students outside of the classroom,
00:29:29
Speaker
What I get out of that is just the benefit of diversity, right? You're only one person and and as one person, you're going to think some subset of things. But as soon as you got a bunch of other people working with you, they're going to show you angles to problems that you haven't even thought of. yeah And that's sort of the magic of working with large groups of students in the c four lab.
00:29:52
Speaker
All right. So why should a student come to the iSchool? What is it that we do that's just really different, that's unique? o Well,
00:30:04
Speaker
um
00:30:09
Speaker
I have a particular...
00:30:14
Speaker
I have a particular answer. My answer might not be anyone else's answer. i'm ah I'm a computer scientist, right? I come at things with a fairly technical approach, computational thinking, right?
00:30:27
Speaker
But ah I really – things that drive me are the human problems, yeah right? So i really bring my more technical approach to human problems. And that doesn't mean i reject other modes of thinking. In fact, i love to be exposed to those other modes of thinking because that's the grist that helps me to learn over time. So, you know,
00:30:56
Speaker
Many of the things, many of the the the new technologies we're grappling with as as as a society, um as a country you know, what makes them hard is not the technology. It's how the technology meets people. Yeah. Right? what All these systems, all these human systems we have are going to change dramatically. Right? You can talk about.
00:31:24
Speaker
the future of work. You can talk about AI in medicine. you could talk about education, right These are all the kinds of problems that the iSchool deals with.
00:31:37
Speaker
And really nowhere else do you find that, you know, academics that are solely focused on that that that interstitial layer between technology and humans. And we come to it, many of us come to it with a somewhat more technical background, but also an awareness of social sciences and theories. And so that blend of things is really unique.
00:32:07
Speaker
And I think maybe one of the most important places people can be working right now. You can throw up your hands and say, uh-oh, what's going to happen with you know the job market, with AI? What's going to happen with crypto? Well, yeah, that's exactly what we're studying. That's exactly what we do in the AI school.
00:32:32
Speaker
Yeah, and to add to that, this is the place where a computer scientist like you can work with somebody who does completely social-focused work.
Interdisciplinary Collaboration at iSchool
00:32:43
Speaker
And so you're going to get those other perspectives that you were talking about earlier, right that you know the way you think might not be the way I think or might not be the way Ingrid thinks or might not be the way Steve thinks. And having all of us together in the same room sometimes generates surprising results. Right.
00:33:02
Speaker
Exactly. So Josh, do you have any last words of wisdom or semi-wisdom for us? yeah i don't know. I'm not the wisest guy in the world.
00:33:12
Speaker
um I don't know. ah
00:33:18
Speaker
I think –
00:33:26
Speaker
The world is scary right now for lots and lots of reasons. But um very often what you see in the media is sort of dismal.
00:33:39
Speaker
It's like, oh, gosh, this is just how is this ever going to work? um ah And you got to be you got to steel yourself a little bit to walk into it because you see some pretty some pretty scary things.
00:33:55
Speaker
But I think when you get close to what's actually going on, when you move through the media narratives that are designed to alarm so as to engage, yeahp you find it's actually a little bit more nuanced than that.
00:34:12
Speaker
There's a lot of interesting stuff, a lot of great potential, a lot of scary stuff. um But I think if that if there's a word of wisdom there, it's don't believe the hype.
00:34:26
Speaker
Look carefully. try to understand what's going on Try to engage with the new technology. Don't get beaten into submission and hide.
00:34:38
Speaker
Yeah, I guess. So one of the things that I think about based on what you were just saying or that that comes to mind for me is AI is a new technology, but we have weathered many new technologies. And a lot of times it's not so much that things are better or worse, they're different. And we've got to be prepared for that differentness.
00:34:57
Speaker
Yeah, I think that's right. i You know, i just as ah as a coda to that, I was part of a little email thread, my my father. And one of the one of his his friends said, well, here's Hal. Hal is here now. But but but that's that's not right. it's not It's not that Hal is here right now.
00:35:19
Speaker
And it's not that AI is going to take every single job. There's so much more beyond that horizon.
AI as a Creative Tool
00:35:28
Speaker
I feel sometimes like with ai I've gone from you know playing you know the the cello to being a conductor of an orchestra. yeah Or instead of taking a trip on foot, I now have a pocket Learjet and I can travel vast different distances. Yeah.
00:35:48
Speaker
So it amplifies my creativity and my ability to do things in ways that are completely unique. um And so there's that flip side. And I think that I'd i'd love for people to approach it with some of that.
00:36:05
Speaker
Great. Josh, thanks for coming and talking to us today. think it's been great. Yep. Thanks. Thanks.