Speaker 1: Welcome to tech stuff. This is the story today. A
Speaker 1: conversation with Yasmin Green, the CEO of Jigsaw. Jigsaw is
Speaker 1: a unit within Google that focuses on finding ways to
Speaker 1: harness technology as a solution for societal issues at a
Speaker 1: global and local scale. The Jigsaw team doesn't have any
Speaker 1: revenue goals, and Google grants it the time and resources
Speaker 1: to study intricate problems around the world, from scient extremism
Speaker 1: to political censorship, to determine why it's happening and what
Speaker 1: could be done about it, all from a technology and
Speaker 1: solution oriented point of view. Jigsaw's most recent project took
Speaker 1: Yasmin and her team to a small city in Kentucky
Speaker 1: called Bowling Green. The challenge was to use AI tools
Speaker 1: to create a town hall meeting for the technological age,
Speaker 1: civic engagement that can be seen, heard, and considered all
Speaker 1: beyond the walls of city hall. I've only has been
Speaker 1: green for many years, and in an age where big
Speaker 1: tech consistently drives negative headlines, I wanted to hear from
Speaker 1: someone on the inside working to find best case scenarios
Speaker 1: for the deluge of new tools and technologies that we're
Speaker 1: now contending with. We sat down to talk about Bowling
Speaker 1: Green and Jigsaw's past and future ambitions. He has been Green.
Speaker 1: Welcome back to Well, last time we spoke was on Sleepwalkers.
Speaker 1: This is tech stuff, but welcome back to the studio.
Speaker 2: Follow you around from podcast to podcasts.
Speaker 1: I think it's the other way around. Tell me a
Speaker 1: bit about Jigsaw and your mission there.
Speaker 2: Yeah, Digsaw is an incubator inside Google. We developed technology
Speaker 2: to give people voice and choice in the world around them.
Speaker 2: And I have the privilege of being the CEO.
Speaker 1: And last time we met you at the director of research,
Speaker 1: and now you're the CEO.
Speaker 2: I have broad a shoulders. Now my voice is a
Speaker 2: little lower.
Speaker 1: But what have you brought to the new role? Like,
Speaker 1: how have you put your stamp on the organization?
Speaker 2: So I came to New York actually fourteen years ago
Speaker 2: to help start Jigsaw. So I feel like I've been
Speaker 2: while I bleed Jigsaw. I've been with jigsaf from the
Speaker 2: beginning and then three years ago I took over as
Speaker 2: CEO and it just coincided with a time of incredible
Speaker 2: change and volatility. But one of the things is the
Speaker 2: kind of mainstreaming of conversational AI and both I think
Speaker 2: the power of the air models and also the kind
Speaker 2: of public discourse and public awareness about AI, and that
Speaker 2: really changed. I think also the tempo inside all of
Speaker 2: the tech companies around releasing and iterating and you know,
Speaker 2: the near term gain is really clear, and then maybe
Speaker 2: the long term pain is less in focus. But that's
Speaker 2: kind of what we think we exist to do. A
Speaker 2: jigsaw is be where the major tech companies and Google
Speaker 2: aren't what our kind of highest and best use would
Speaker 2: be are the things that other people aren't doing right now.
Speaker 1: Last time we met, we talked about a program you
Speaker 1: ran to serve targeted ads to people considering the path
Speaker 1: of terrorism.
Speaker 2: Right, yeah. Yeah. When we started working on radicalization, which
Speaker 2: was one of our first focus areas over a decade ago,
Speaker 2: the idea that the Internet would have anything to do
Speaker 2: with why anyone would go and join the majorhaden in
Speaker 2: Afghanistan was so unfathomable, both to people who are experts
Speaker 2: about you know, Islamism and the people who are experts
Speaker 2: in the Internet. Now, those you know, areas of kind
Speaker 2: of extremism and hate, and you know, those things are
Speaker 2: they are not solved problems, but they're also not in
Speaker 2: anyone's behind spots like they are very crowded that the
Speaker 2: subject of regulation, et cetera. And now we're much more
Speaker 2: interested in, like, what if this all goes right and
Speaker 2: we do build this incredibly powerful technology and it's prevalent,
Speaker 2: how do we make sure that people have a voice
Speaker 2: and choice in the world around them, because it's not
Speaker 2: inevitable that that will come along with the intelligence.
Speaker 1: How did your sort of personal experience prepare you for
Speaker 1: this role? I know you worked to Google beforehand, I
Speaker 1: think in sales in Middle East and Africa and Europe,
Speaker 1: and I know that you left Iran I think as
Speaker 1: a young child as well. I mean, how how has
Speaker 1: your kind of personal experience contributed to you being the
Speaker 1: technologist that you are today based.
Speaker 2: In Paris for Google, managing strategy and ops for the
Speaker 2: sales teams. And I got, of course, thing, do you
Speaker 2: want to come to New York and help start Jigsaw?
Speaker 2: And the original frame of Jigsaw was actually kind of
Speaker 2: looking at geopolitical threats. The revolution that happened in Iran
Speaker 2: which led to me leaving, where the religious extremists took over.
Speaker 2: They were those were violent extremists, you know, and sometimes
Speaker 2: they do they make big moves and they take over land,
Speaker 2: and they can you know, oppress people. For such a
Speaker 2: long time, we had this view that was prevalent in
Speaker 2: tech then, which was, you know, the Internet will be
Speaker 2: a democratizing technology. I don't have to worry it's going
Speaker 2: to be bumpy along the way, but like, one thing
Speaker 2: you can be sure is this is going to bring
Speaker 2: people together and connect them to information. And I was like, yeah,
Speaker 2: I think there are a lot of people going to
Speaker 2: have their own idea about that, and so I wanted
Speaker 2: to work on things to do with the Internet that
Speaker 2: were very kind of discerning about what might happen. So
Speaker 2: I think this idea of alays looking around the corner
Speaker 2: and saying where's where's crowded now and where people aren't looking,
Speaker 2: and having the luxury to kind of have this incredible
Speaker 2: group of people be thinking about what's going to happen next,
Speaker 2: and you know, having the resources of Google to try
Speaker 2: to affect that.
Speaker 1: Jigsaw is a division of Google with I don't think
Speaker 1: has any revenue. Doesn't have a revenue line, right, It's a.
Speaker 2: No, it does not.
Speaker 1: And Google's now in this moment where I think we'll
Speaker 1: have a lot more questions about the larger role of
Speaker 1: technology companies in our society. But then also the business
Speaker 1: model with search and with the rise of AI is
Speaker 1: more threatened. And so how does an organization like the
Speaker 1: one you lead deal with those two things? On the
Speaker 1: one hand, maybe being odds or surfacing things at the
Speaker 1: parent company doesn't want to be surfaced. On the other hand,
Speaker 1: operating in a more economically constrain the environment where there
Speaker 1: are cuts and buyouts and stuff going on. How do
Speaker 1: those two things affect you up day to day.
Speaker 2: I mean, there are so many parts of all the
Speaker 2: tech companies that work on the things that are challenges.
Speaker 2: You know, they will have these massive trust and safety
Speaker 2: machinery that are actually looking all the time and like
Speaker 2: a trillion pieces of content and everything from child sexual
Speaker 2: assault imagery to the you know, the worst of it
Speaker 2: and scams. So the tech companies are full of people
Speaker 2: who are looking at where things aren't going well and
Speaker 2: trying to affect them. I think maybe the thing that's
Speaker 2: different is that Jigsaw is providing a public commentary and
Speaker 2: there I think it probably helps that we have our
Speaker 2: own brand, and so we go off and do our things,
Speaker 2: and sometimes sometimes we try things that don't work, and
Speaker 2: in which case Google's not too bollied. And then when
Speaker 2: we do things that you really are very significant contributions
Speaker 2: to helping make people better off with the Internet and AI,
Speaker 2: then Google's like, this is Google Jigsaw everyone, we take
Speaker 2: full credit. So it seems to be a good kind
Speaker 2: of arrangement that really works for Google. But you're right
Speaker 2: that we often are in that we are looking at
Speaker 2: like what what is not necessarily going to go? Well,
Speaker 2: it is in Google's long term interest to have a
Speaker 2: group that does that and that makes significant progress that
Speaker 2: they can rightfully take credit for.
Speaker 1: You're sort of in a sense you're not red teaming
Speaker 1: particular products, but you're read teaming the cult that the
Speaker 1: wider tech industry.
Speaker 2: Yeah, and e specifically with the view to like, what
Speaker 2: are we going to do about it? Yeah. I assure
Speaker 2: you that there are many many teams that are red
Speaker 2: teaming internally. They just don't come and talk to you
Speaker 2: about it, or maybe they can maybe actually got an
Speaker 2: advocate work. It was so great. That was really great.
Speaker 1: Can you describe for our audience that text stuff, what
Speaker 1: ethnographic research looks like and how you used it. If
Speaker 1: you're projecting Bowling Green Kentucky.
Speaker 2: Yeah, depending on where you sit, you then press that
Speaker 2: jigs or does it or you're kind of appalled that
Speaker 2: not every part of a tech company does this. But
Speaker 2: it's basically the very patient type of research that involves
Speaker 2: going in and spending time with people and observing how
Speaker 2: they live in their community and people are making sense
Speaker 2: of using the Internet, which is the part we care
Speaker 2: about as part of an existence that extends beyond technology obviously,
Speaker 2: like who they are, what their identity is, what they do,
Speaker 2: who their family is with their community is how they identify,
Speaker 2: and so we bring that wide angle lens to how
Speaker 2: we think about challenges. So we had built this big AI,
Speaker 2: really helpful tool that was used by platforms and publishers
Speaker 2: around the Internet to try to have conversations that serve
Speaker 2: their communities. But last year we thought to ourselves, maybe
Speaker 2: we could help enable conversations, large conversations that happen not
Speaker 2: just online between people, but between policymakers and their constituents.
Speaker 2: And we end up doing something which we've just completed,
Speaker 2: which I'm excited to talk about in Kentucky, in this
Speaker 2: town called Bonling Green. But we kind of understood that
Speaker 2: they wanted to have a town wide conversation, but that
Speaker 2: nobody participated. There was no public input when they were
Speaker 2: given space to contribute.
Speaker 1: In fact, the only place you obviously a town hall
Speaker 1: in America Arizona, Cinno, Fox News too use for the election, right.
Speaker 1: The concept of the town hall is like more or
Speaker 1: less a cliche. So how do you use technology to
Speaker 1: actually have what would have the town hall in premodernaty?
Speaker 2: Actually? Okay, So we went to meet with the judge
Speaker 2: executive of this county. It's like the mayor of this
Speaker 2: county who's called Doug Norman. Incredible, I'm gonna call him Doug.
Speaker 2: This town is a doubling in size over the next
Speaker 2: twenty years. It's going from one hundred and forty eight manation.
Speaker 2: Might not be getting those rights those numbers right, it
Speaker 2: might be doubling even more. But it's basically like adding
Speaker 2: another bowling green to the county, which sounds great like
Speaker 2: growth and jobs and development. And at the same time
Speaker 2: we're a mixed bag of emotions in the community. So
Speaker 2: there's a big farming constituency and they are concerned about
Speaker 2: land use. In the irreversible decision to let people develop
Speaker 2: on your land, lifelong residents are concerned about the preserving
Speaker 2: their culture and their building, and there's a lot of
Speaker 2: concern about migration. And it's interesting that in Kentucky and
Speaker 2: Bowling Green the concern is not about migration from foreigners
Speaker 2: but from people from Nashville, Tennessee. Really, they're like, we
Speaker 2: don't want to become like Nashville extended here. So there's
Speaker 2: all like valid concerns that they have, and they're going
Speaker 2: to have to have all you know, all this growth.
Speaker 2: And actually the words from Doug capture it best. He
Speaker 2: said to them, do we want this growth to happen
Speaker 2: to us or do we want the growth to happen
Speaker 2: for us? If we want to chappen for us, we
Speaker 2: have to come together and set a vision for Bowling Green,
Speaker 2: which is really amazing. But then you are similarly perfectly,
Speaker 2: how's it going like with you know, Townholls, And he's like, yeah,
Speaker 2: we get about eight or nine people, and so we're like,
Speaker 2: maybe we can help because we have a lot of
Speaker 2: experience with tech and large girl conversations. But it always
Speaker 2: starts like product them always starts with like field work.
Speaker 2: So we went to Bowling Green to go and spend
Speaker 2: time with people, people who are really marginalized from politics,
Speaker 2: like the elder, these students, recent migrants, et cetera, people
Speaker 2: who've just been left incarceration, to figure out what what
Speaker 2: would stop them from participating, And so we do things
Speaker 2: like we took a couple to like a tree planting meeting.
Speaker 2: We took like this college graduate to a city planning
Speaker 2: commission get together about whether someone should be allowed to
Speaker 2: add to the back of their pub. We took all
Speaker 2: of them and so you can observe what is holding
Speaker 2: them back, and it was the things that they said
Speaker 2: resonated so much with me. They don't think that there
Speaker 2: they have the expertise, they don't think they matter, they
Speaker 2: don't think that if they use their voice it would
Speaker 2: be listened to. And it's all stuff that who hasn't
Speaker 2: been in like a large meeting where you're like, I
Speaker 2: don't really understand what we're talking about, or I don't
Speaker 2: understand my role in this discussion, or like if I
Speaker 2: use my voice, you know my boss going to hear me,
Speaker 2: or is it going to go anywhere? So if we
Speaker 2: can relate to it, they get that level. The fact
Speaker 2: that people really show up to their local community, whether
Speaker 2: it's like their refugee community or their LGBTQ community, or
Speaker 2: the sports club or the church. But then when you
Speaker 2: like come to the town conversation, the civic oh, I
Speaker 2: don't know, I don't look or sound the part. You know,
Speaker 2: the college student we took to the city planning commission thing,
Speaker 2: he was like, I can't go to that. I'm not
Speaker 2: a lawyer. And of course when he went there were
Speaker 2: no lawyers. But like, that's what we have in our head,
Speaker 2: is like, I am the expertise.
Speaker 1: Can you tell me a little bit more about perspective API.
Speaker 1: I know it's developed by Jigsaw and it's now used
Speaker 1: by Wikipedia and Wall Street Journal, And essentially what it
Speaker 1: does is regulate the comments sections using AI to understand
Speaker 1: the impact any given comment might have on other commenters
Speaker 1: or participants and score that impact. But I'm curious, is
Speaker 1: the Bowling Green Project an extension of Jigsaw's work on perspective.
Speaker 2: It's hard to even remember that, you know, nine or
Speaker 2: ten years ago, all these publishers are closing their comment
Speaker 2: spaces because they couldn't even have a civil conversation, and
Speaker 2: so Perspective was a you know, an AI that helps
Speaker 2: with managing online conversations. They get ranks all different types
Speaker 2: of things, everything from like ad hominem attacks too, is
Speaker 2: something constructive and what explained like it scores them for
Speaker 2: publishers to decide what kind of conversation you want to have.
Speaker 2: But in the case of policymakers in there, you know
Speaker 2: the people they represent, they need to be in conversation too.
Speaker 2: There's this quote that I love, which is that conversation
Speaker 2: is the soul of democracy. What gives democracies legitimacy is
Speaker 2: our ability to have a free and open exchange of ideas.
Speaker 2: And so the thing that we wanted to help Doug with,
Speaker 2: which kind of informed what we developed, which we call
Speaker 2: sense making, was he wants to tell his people come
Speaker 2: and be in a conversation with me. People weren't showing up.
Speaker 1: So it wasn't like there's an existing conversation happening. How
Speaker 1: do we make it more provatal inclusive? It was how
Speaker 1: do we create the environment for a conversation to happen?
Speaker 2: Yes, and they need to believe that he will hear
Speaker 2: their voice if they express it. People who study this say,
Speaker 2: if there's nothing that's worse than not asking people for
Speaker 2: their opinion.
Speaker 1: Asking people ignoring them because that's truly demotivating.
Speaker 2: So we realized that he would need to be able
Speaker 2: to tell them that their voice would be heard, and
Speaker 2: so we worked. We ended up working with a platform
Speaker 2: called polist to solicit input and it was just open ended,
Speaker 2: open ended like answers, so it was really in your
Speaker 2: own words and on your own terms, So people were
Speaker 2: free to say what they wanted. They're free to do
Speaker 2: it at their own time, in their bedroom, you know,
Speaker 2: an anonymously. So it was really like a lot of
Speaker 2: what we heard in the ethnography we were trying to
Speaker 2: account for. My team sat down with two sisters that
Speaker 2: were recently relocated Bowling Green from Afghanistan, and they went
Speaker 2: and in their home like with like over tree. Then
Speaker 2: they were like you know, with the headscarves everything. They
Speaker 2: were asking them like what what would it take for
Speaker 2: you to come and be part of this conversation And
Speaker 2: they were just like that, they had no idea what
Speaker 2: civic participation referred to, the idea of like even attending
Speaker 2: a town hall, never mind being asked to speak, was
Speaker 2: a complete anathema to them. So this conversation that we
Speaker 2: set up, which is called what could Bowling Green be,
Speaker 2: which we did with this incredible partner on the ground
Speaker 2: called Innovation an Engine. The conversation was like designed to
Speaker 2: be like you are free to say what you want,
Speaker 2: where you want, whenever you want. And it was a
Speaker 2: month long conversation and we did it through this POSE
Speaker 2: platform and the idea was speak and you'll be heard.
Speaker 2: And the part that we used AI for was making
Speaker 2: sense of what everybody said in a way that could
Speaker 2: be shared back to them, that they would see their
Speaker 2: voices in the conversation and could be shared back with
Speaker 2: the policymaker so he could take an action.
Speaker 1: After the break, the largest town hall in US history
Speaker 1: and how Jigsaw made it happen. Welcome back to tech stuff.
Speaker 1: Before the break, we were talking with Yasmin Green, CEO
Speaker 1: of Jigsaw, about how the initial pieces of an ambitious
Speaker 1: project using AI to create a more inclusive and active
Speaker 1: town hall came together in the town of Bowling Green, Kentucky. So,
Speaker 1: taking a few steps back, how does this come about? Like,
Speaker 1: how do you choose a community to partner with? And
Speaker 1: I guess what are the stakes for you to be
Speaker 1: working with government versus just in a purely online arena,
Speaker 1: Like how much pressure and also opportunities.
Speaker 2: Does that add? Kentucky's actually a really pioneering state, and
Speaker 2: partly a little bit by it because now I spent
Speaker 2: much time there, but the cross even not in Bonling Green,
Speaker 2: other places do. They're doing a lot of civic tech
Speaker 2: work and they're very forward. And Bowling Green in particular
Speaker 2: was known because they had done something several years ago
Speaker 2: where they had done public participation with the local newspaper. Okay,
Speaker 2: and we had heard that they're putting together the stake
Speaker 2: vision for twenty fifty.
Speaker 1: So what do we want Bowling Green? Yeah, so you'd
Speaker 1: heard about this, you knew that they would take forward.
Speaker 2: Yes, and we're like, maybe we can actually they come
Speaker 2: and give them something really useful. Maybe we can co
Speaker 2: develop something with them. And so they said, okay, let's
Speaker 2: do this month and conversation to hear what people have
Speaker 2: to say. And then they actually did like super local
Speaker 2: like ground game marketing campaign.
Speaker 1: Where you involved with a marketing campaign or.
Speaker 2: Yeah, we partnered with them, but it was like local
Speaker 2: creative agencies that came up with the creators.
Speaker 1: So basically get people to be aware and think it
Speaker 1: was fun, evangelize interact with the platform.
Speaker 2: Yeah, yeah, and exactly, And the main thing was just
Speaker 2: to be local. So things were translated into nine languages.
Speaker 2: They they put them in you know, they fly at
Speaker 2: international supermarkets, they went to make a church is, they
Speaker 2: went to basketball games.
Speaker 1: So you created awareness and then whether like QR codes
Speaker 1: that people like clicked on their phones and they would
Speaker 1: take it into the conversational platform or like how what
Speaker 1: was the.
Speaker 2: Yeah, basically you look, yes, they're working on code. Do
Speaker 2: you go to what could be GB? In fact, you
Speaker 2: can stop it. What could Bowling Green be? Is the
Speaker 2: website dot com and you can see the report. But
Speaker 2: people would log in and then you just get like
Speaker 2: a question prompt what do you want for Bowling Green
Speaker 2: in the future, Which that's that depended you put your
Speaker 2: answers in and.
Speaker 1: Only one single prompt.
Speaker 2: There's one question and could you could put you could
Speaker 2: keep putting more of what you want. You probably have lots.
Speaker 2: It's pretty open. You might have ideas for education or infrastructure,
Speaker 2: you know, restaurants. But then there's voting, so you you
Speaker 2: vote on other people's input. And that was a really
Speaker 2: important signal to see like where, if anywhere, do we
Speaker 2: agree right and where we put so much effort into
Speaker 2: trying to make a like make this local and be
Speaker 2: make it accessible and still like you know, the beginning
Speaker 2: of the month long conversation, we were like, is anyone
Speaker 2: is anyone going to turn up to this? Yeah? Because
Speaker 2: maybe we do all this effort and it's eight people.
Speaker 1: Did you show a tumble.
Speaker 2: Right? Right? So the baseline's eight okay, So we were like,
Speaker 2: thirty will be good. Thirty will be good. Now, so
Speaker 2: we're like, if we could get a one thousand or
Speaker 2: two thousand, and so it was eight.
Speaker 1: Thousand, eight thousand respondents.
Speaker 2: This kind of like AI enabled town hall was a
Speaker 2: thousand times larger than the regular town hall, and one
Speaker 2: in every ten residents basically the equivalent of one every
Speaker 2: ten residents participated. So as far as like that's what
Speaker 2: the folks on the ground and say, is that it
Speaker 2: meant that people were discussing this topic even outside of
Speaker 2: the actual platform because there was that much participation, So
Speaker 2: it kind of pas the tipping point. So it was
Speaker 2: really nice to see the numbers go up. They did
Speaker 2: go up, they did spike a bit towards the end,
Speaker 2: and it was actually the largest digital town hall in
Speaker 2: US history. So it's the most that anyone's come together,
Speaker 2: and that's US partnering with a town in Kentucky. So
Speaker 2: if you like imagine what is possible at the state
Speaker 2: level or across states if you bring people together to
Speaker 2: share their view. Although there was before like advocating for
Speaker 2: more of those type of CIF conversations, I think the
Speaker 2: first big apprehension was is anyone could show up? Ye?
Speaker 2: The second was, well, you know, if this is a
Speaker 2: scaled version of an in person town hall, we might
Speaker 2: kind of be in trouble because the eight people that
Speaker 2: come to an in person town hall detractors detracted typically, right,
Speaker 2: the ambivalent people don't go and show up, or people
Speaker 2: who are like quite like the idea and also could
Speaker 2: see why not the idea? That's not the people that
Speaker 2: get you motivated to get off the counch and drive
Speaker 2: all the way and wait there. It's usually people who
Speaker 2: really don't want the thing to happen. So I think
Speaker 2: local leaders were like, well, we're just going to get
Speaker 2: eight thousand of them entrance, you know, because they don't
Speaker 2: want to scale in that sense, And that was why
Speaker 2: it's so important to do like such a broad marketing
Speaker 2: campaign to like all these different constituencies, but the bigger
Speaker 2: side of relief from local leaders when they looked at
Speaker 2: their feedback and a lot of stuff they anticipated. There
Speaker 2: was like in a growing town, you need more infrastructure,
Speaker 2: you need more roads. Lots of really interesting thoughts on education,
Speaker 2: on like restaurants. There was just one submission on the
Speaker 2: restaurant and interesting kind of like unexpected proposals on a
Speaker 2: number of fronts, but the one that makes me giggles
Speaker 2: in the area of like food and restaurants. One person
Speaker 2: submitted an idea to pass a law to mandate restaurants
Speaker 2: to include ketchup saches in the takeaway like the takeaway
Speaker 2: bags of like fast food restaurants. So you know, that's
Speaker 2: how you know that people are really expressing their views.
Speaker 2: And we've got good mixed views but didn't have much support.
Speaker 1: How did people express support for one another? Like I
Speaker 1: go on the platform, answer this is you know, I
Speaker 1: want more free ketchup saches? Is that that Now I
Speaker 1: don't live in Boling. I read that it was Geo
Speaker 1: Geo fence. Otherwise it would have been me. But then
Speaker 1: is that then like available for other people to comment on? Okay,
Speaker 1: so it's like a message boarders, it's not just a one.
Speaker 2: So they're routing so you you put your idea and
Speaker 2: then they're routing ideas for you to vote on. And
Speaker 2: it's actually much You get a much higher percentage of
Speaker 2: people like the The votes are much higher than the
Speaker 2: actual submissions. Yeah, because it's much easier to vote and
Speaker 2: you're kind of in there and it's a little addictive.
Speaker 2: And I think that's one of the really really cool
Speaker 2: things about this conversation is that it was designed to
Speaker 2: advance a very large conversation. It wasn't designed to have
Speaker 2: like eight thousand and one on ones. That's not what
Speaker 2: we're trying to do when we're trying to have a
Speaker 2: long conversation. And that that's interesting because there isn't the
Speaker 2: option to reply the way that purchases platform is set up.
Speaker 2: You have an idea, and you can vote on other
Speaker 2: people's ideas, and if you really don't like the catch
Speaker 2: up idea and you actually think it should be mustered,
Speaker 2: put your idea in the area. Yeah, but we're not
Speaker 2: you're not telling you know, you're not going to get
Speaker 2: into some you know, back and forth with the person
Speaker 2: who suggested Ki jump is you know, from the wrong
Speaker 2: part of town, and everyone's asking that part of town.
Speaker 2: You know, here's no point going down that you know,
Speaker 2: spiral of the ad hominym attacks, et cetera. That happens
Speaker 2: a not on social media. So the conversations dis like
Speaker 2: designed differently from the get go that you put your ideas,
Speaker 2: you vote, if you want to keep going after you've
Speaker 2: given your thumbs up your thumbs down, you're welcome to
Speaker 2: put your idea in there. And it well. So then
Speaker 2: when we had to take stock of like, well did
Speaker 2: any of these ideas they resonate with other people? So
Speaker 2: there were four thousand policy proposals and over half of
Speaker 2: them had near universal support.
Speaker 1: Near universal support over half. So in other words, what
Speaker 1: you take from it is the community knows what's good
Speaker 1: for it.
Speaker 2: Well yeah, and and that you know, there are so
Speaker 2: many opportunities for us to hear from the people who
Speaker 2: are the angriest or at least well informed.
Speaker 1: Or did you know the voter registration of the participants
Speaker 1: or I mean.
Speaker 2: Was this no, but it's it's it is a it's
Speaker 2: a purpolish place, so it's quite a mix. And you
Speaker 2: saw some of the things that you see you know, nationally,
Speaker 2: but most of the things that local people care about
Speaker 2: are you know, what is going to happen to their
Speaker 2: K through twelve education. It turns out they want more
Speaker 2: vocational skills, you know, like they want their university, which
Speaker 2: is the pride of like you know, Western Kentucky University,
Speaker 2: they want it to be more integrated into their economy
Speaker 2: and their like workforce development. And there's a lot of
Speaker 2: things that they want. And the perspective of not just
Speaker 2: dog actually but even like private section leaders was like,
Speaker 2: that's great. Now we have now we can go and
Speaker 2: lead and build and make changes knowing that we have
Speaker 2: broad public support. Otherwise, if you're just based basing you know,
Speaker 2: your like sentiment gauging on public calls and social media,
Speaker 2: just thinking that like no one wants anything to happen,
Speaker 2: you know, or that like you might have some supporters,
Speaker 2: but also like everyone else is an avid, you know,
Speaker 2: avid distractor. So so I think those are two really
Speaker 2: important essons. One people really will show up, they really
Speaker 2: do want to shape their future, you know, And two
Speaker 2: that there is more that unites us then divides us,
Speaker 2: which is kind of imperative for us to keep you know,
Speaker 2: in our minds and cultivate at this moment.
Speaker 1: And what was Jigsaw's role or technology deployment because the
Speaker 1: marketing campaign was like panel by local agencies, the polished
Speaker 1: platform was it's not a Jigsaw platform, right, So what
Speaker 1: did what was the layer that you guys added It was.
Speaker 2: The making sense of the conversation. So if you can
Speaker 2: get lots of people to contribute, how can you guarantee
Speaker 2: that their voice will be heard? And so it really
Speaker 2: is using this case, Google's aigemini using an eye for
Speaker 2: what it does best, which is they take large amounts
Speaker 2: of information that it expressed in a very local and
Speaker 2: personal way, understand it, organize it, they sort it, prioritize it,
Speaker 2: and then give it back to people. So like, you
Speaker 2: know the thing if you go to what could bowlinggreenpeed
Speaker 2: dot com, you'll see the report and it's like, you know,
Speaker 2: Google Maps, we think at Google mapps, you started very
Speaker 2: high level. If you'll look at like the states of
Speaker 2: in America, that's the first thing that you see, and
Speaker 2: then you're like, actually, I'm kind of interested in New York.
Speaker 2: And then you're like interest in my neighborhood and they're interesting.
Speaker 2: You're like, oh, how can you see my front door?
Speaker 2: You know, And that's how it is with this map
Speaker 2: of opinions, like not geographical terrain, but opinion terrain. But
Speaker 2: you start the top and you're like, what do people
Speaker 2: have born and green care about? And then it has
Speaker 2: those you know, instructural education, et cetera. And then you're
Speaker 2: driven You're like, actually, what I really care about is
Speaker 2: you know, education, and then go through, you know, you
Speaker 2: just keep digging and most people, most people have specific
Speaker 2: areas of interest. But the truth is people want to
Speaker 2: know where they sit relative to their neighbors, and so
Speaker 2: people want to actually find where they are. Because there's
Speaker 2: this plot graph on there that has a distribution of
Speaker 2: all the proposals and how much agreement they got, and
Speaker 2: so you can see am I like on the am
Speaker 2: I the fringe opinion holder even though like me and
Speaker 2: all my two friends agree with me, or actually is
Speaker 2: something that I've been scared to say out loud, something
Speaker 2: that a lot of other people believe. So you can
Speaker 2: go in there and see that, which is really powerful.
Speaker 2: And of course most people agreed with most things. So
Speaker 2: I think it does restore our sense of you know,
Speaker 2: like it's a funhouse of mirror sometimes social media where
Speaker 2: you're like, oh my goodness, everyone's extreme. It's just me
Speaker 2: here that doesn't want to have like an extreme mistake
Speaker 2: on things.
Speaker 1: What are the key policy ideas that have emerged and
Speaker 1: will you measure success based on whether or not they're
Speaker 1: enacted or are you, in a sense now passing the
Speaker 1: bat on the lull.
Speaker 2: It's definitely in the court of their leaders, and it's
Speaker 2: their process so to begin with. So I think they
Speaker 2: genuinely do want people's input because the process kind of
Speaker 2: proceeded our involvement. But one example was they had wanted
Speaker 2: to do a riverside development. They built building up the
Speaker 2: area along the riverside, and when they had got tried
Speaker 2: to get public input, very few people pay and they
Speaker 2: were detractors. But then if you look at the feedback
Speaker 2: on that one topic in the reports, like seventy five
Speaker 2: plus percent of people support it. So I think they're
Speaker 2: now going to accelerate that. What we did, in terms
Speaker 2: of like really trying to gauge people's sense of like
Speaker 2: whether it mattered for them, was we say it on
Speaker 2: whether they felt their voice mattered. We asked them whether
Speaker 2: they felt they understanded other people's perspectives better than they
Speaker 2: did before. And then third was where they think they
Speaker 2: believe their input will lead to a better outcoming. On
Speaker 2: all of those, it was like eighty percent plus satisfaction.
Speaker 1: And were you surprised by the outcome? And how do
Speaker 1: you how do you kind of measure the success of
Speaker 1: a project like this.
Speaker 2: I think it was that that people wanted to come
Speaker 2: out and they did. I think that there's two There's like,
Speaker 2: there's two parts of my brain. There's a part of
Speaker 2: brains like did we achieve our you know, like our
Speaker 2: product development goals or are like whatever engagement goals? And
Speaker 2: then there's an other part of me that's like can
Speaker 2: I retain my hope for like society thriving as we
Speaker 2: get more at Amize And so I feel like on
Speaker 2: both of those I felt really good. I felt good
Speaker 2: because obviously the technology wor I also, you know, the
Speaker 2: sense of the team is that we can push to
Speaker 2: do more. And so now we're exploring what other types
Speaker 2: of conversations in the conversation space. How could we Maybe
Speaker 2: it's a different scale, you know, maybe it's a different
Speaker 2: nature of conversation. Maybe we're using different capabilities of Gemini
Speaker 2: to do more to bring people together, to have them
Speaker 2: understand each other more and then having it count for them.
Speaker 2: So that's the kind of fun and exploration phase that
Speaker 2: we're in. Now.
Speaker 1: There's another thing that came out of the research I
Speaker 1: think called grounding.
Speaker 2: Yeah, can you talk a bit about rounding. Groundings their
Speaker 2: sister to hallucinations, So, which is a funny term that
Speaker 2: has widespread adoption to describe when air models may make
Speaker 2: stuff up.
Speaker 1: Because of course that the very bad scenario. Who would
Speaker 1: be that the air was making up policy of pro
Speaker 1: puzzles and no one asked for.
Speaker 2: Oh my goodness, imagine that in such a high stakes
Speaker 2: context where people are trying and talk to their policy maker.
Speaker 2: And there were some funny things that in our early
Speaker 2: testing we were finding it would kind of conflate two
Speaker 2: topics that seem like they're about the same thing, or
Speaker 2: that like you would it would have only one. There'd
Speaker 2: be like one post about you know, there should not
Speaker 2: be some any sweet tuitets offered at in kindergarten. And
Speaker 2: then the summarizing AI would be like the people of
Speaker 2: Bowling Green, you do not want to have like cakes
Speaker 2: and chocolates and candy and they would rather have like
Speaker 2: keep compisation. It's just like wait, whatever that wasn't said, yos,
Speaker 2: that's out of lines. So part of kind of making
Speaker 2: sure that we understood the ways that the AI might
Speaker 2: misfire was doing a ton of testing with humor reviews
Speaker 2: and on previous data sets, and then we did it
Speaker 2: kind of kind of like a small private trial a
Speaker 2: few months earlier with Bowling Green and the local partners
Speaker 2: reviewing them and just seeing like how might it go wrong?
Speaker 2: And then some of the tweets we made with to
Speaker 2: make sure that we batched appropriately. So grounding is every
Speaker 2: output that the AI produces is that it's like appended
Speaker 2: with citations. So everything that the AI says was a
Speaker 2: theme in the report, you can say, you know, show
Speaker 2: me receipts and it will take you to the underlying
Speaker 2: policy proposals or words, the actual words of the people
Speaker 2: at Bowling Green, so you don't have to you know,
Speaker 2: you actually don't have to trust the AI.
Speaker 1: Super interesting. Could grounding be used beyond this context? Do
Speaker 1: you think?
Speaker 2: Yeah? Yeah, and it is it's used broadly. Have you
Speaker 2: ever used notebook M Yes, So they use grounding. So
Speaker 2: when they you know, when they summarize a you can
Speaker 2: give it like one hundred reports or whatever backad Democrat.
Speaker 2: Of course, if you're don't research and it'll tell you
Speaker 2: the themes and then it gives you like like footnotes
Speaker 2: so that you can see where the source material and
Speaker 2: have confidence that at accurate.
Speaker 1: What's the wider hope with a project like Bowling Green?
Speaker 1: I mean, do you is that, like, is there an
Speaker 1: ideal community size? Why this is will be perfect for
Speaker 1: towns that are growing of around one hundred thousand people
Speaker 1: because like you have enough people in the community, you're
Speaker 1: invested in the future because it's growing, it's not so
Speaker 1: large that you get so many proposals that it's like
Speaker 1: impossible to sort. And therefore maybe there's like another one
Speaker 1: hundred Bowling Greens in the US, or is it more like, Wow,
Speaker 1: the greatest problem in US politics today is that participation
Speaker 1: and is low and conversation is toxic, and therefore, like
Speaker 1: this could be a much wider solution, Like how do
Speaker 1: you between those two things? Like where do you feel
Speaker 1: for me?
Speaker 2: The more conversation, the better. I think the reverse is
Speaker 2: also true, the less conversation the worst, the worst we
Speaker 2: ended up. I understand each other, and that was kind
Speaker 2: of where we had taken perspective for over the last
Speaker 2: couple of years, was actually not just helping publisher score
Speaker 2: things that they might want to see less of, like toxicity,
Speaker 2: but actually doing the bridging attributes as well, which was
Speaker 2: the things that kind of help people who disagree stay
Speaker 2: in the conversation together. So I would like to see
Speaker 2: more of it. And I think we've also shown we
Speaker 2: show that the appetite is there from people and that
Speaker 2: it can go really well. That I think the judge
Speaker 2: executive Doug Gorman in our case, was really really brave
Speaker 2: to try and do that. Why brave because the safe
Speaker 2: nobody shows up, or say if they show up and
Speaker 2: you know it's Cook Coffinus. But now seeing what Doug's
Speaker 2: that actually Doug came to So Google has a developer
Speaker 2: conference equal to I Air. That just happened, and Google
Speaker 2: invited Doug to go and participate in the local leaders track,
Speaker 2: and he went and told all these like other mayors
Speaker 2: and local leaders and they were They really lit up.
Speaker 2: And so from what I understand, I think there is
Speaker 2: a lot of appetite to do this, and I think
Speaker 2: him going first and having the bowling green kind of
Speaker 2: proof of concept will inspire others to adopt it to.
Speaker 1: Is that part of your work or in a sense
Speaker 1: you like the fire and it's of other people to
Speaker 1: kindle it bad metaphor, but I mean otherwise do what
Speaker 1: are you going to do the same experiment again in
Speaker 1: another community or a larger community? Or do you feel
Speaker 1: like you have the proof point that you think you know?
Speaker 2: I think it's you know this question, like what is
Speaker 2: Jigsaw's highest and best use. I think if we if
Speaker 2: now we've done the concept, if there's another proof like
Speaker 2: another place that we'd want to do it, I think
Speaker 2: we would be up into that if it felt that
Speaker 2: it was some additive like materially additive, if it were
Speaker 2: you know, maybe at the state level or you know,
Speaker 2: some other kind of complex conversation that we felt that
Speaker 2: we could we could advance. I think there will be others,
Speaker 2: like kind of implementers who will I think this will
Speaker 2: become a kind of a commercial opportunity for others to
Speaker 2: help with. I don't think we do you know, that's
Speaker 2: the best use of Jigsaw to do many many more
Speaker 2: of the same thing in towns.
Speaker 1: So in other words, like a polling firm could take
Speaker 1: it over as a business. Liane Audrey Tang, who I
Speaker 1: know that you were, you know, to spend time with.
Speaker 1: Recently an interview with Nick Thompson and Audrey spoke about
Speaker 1: the Bowling Green project and in the context of how
Speaker 1: in California post fires, Newsome is sort of using this
Speaker 1: tech enabled participatory democratic technology to understand what people want
Speaker 1: from the rebuild of like burnt areas of LA. Where
Speaker 1: does this fit in with the kind of larger tech
Speaker 1: enabled digital democracy trend.
Speaker 2: I think it is. I think they're pioneering there because
Speaker 2: they're doing at the state level in California's a massive state.
Speaker 2: And interestingly, when we did it in Bowling Green, which
Speaker 2: was you know, Well or Warren County, much much smaller,
Speaker 2: super open question. In the case of California, that asking
Speaker 2: a really narrow question. I mean, it's big in itself,
Speaker 2: but it's much more focused question, which is, you know,
Speaker 2: how are we going to where do we go from
Speaker 2: here after the wildfire specifically, So I think that's really instructive,
Speaker 2: Like doesn't once you go to the state level, doesn't
Speaker 2: need to be so focused And they've already got quite
Speaker 2: a bit of engagement and is she going to talk
Speaker 2: to them about how they're going to make sense of
Speaker 2: it because I don't know what they do. Well, yeah,
Speaker 2: we're in touch with them. And Audrey, who's you know,
Speaker 2: it was the former the kind of in agural like
Speaker 2: I guess did a digital minister for Taiwan is like
Speaker 2: this just she's just a global matchmaker. So she's advising
Speaker 2: California as well, and she's advised us a lot. She's
Speaker 2: been very instructive for our work.
Speaker 1: Two questions are close, I guess the first. Obviously, it's
Speaker 1: a moment where Google is being you know, the subject
Speaker 1: of the Justice Department and Chrome. All this kind of stuff,
Speaker 1: in other words, is a target of federal investigation when
Speaker 1: you work with government, like do you are there people
Speaker 1: who say this is a conflict of interest or Jigsaw
Speaker 1: being in bed with local estate governments is somehow advancing
Speaker 1: Google's corporate motives elsewhere, and that's a problem.
Speaker 2: In this case. We didn't actually have any commercial relationship
Speaker 2: with the government. We were actually partnered with the group
Speaker 2: the I told you about Innovation Engine and they did
Speaker 2: the kind of execution. But we did care a lot
Speaker 2: about making the tech work for the hotesty maker and
Speaker 2: the people because it seemed it seems like such an
Speaker 2: important thing for us to get right. And if we
Speaker 2: want policy makers to make good policy, even about tech,
Speaker 2: they should kind of understand it and see how it
Speaker 2: shows up for them. And in this case, I think
Speaker 2: Voluningroom was so attractible was so because it was so
Speaker 2: mixed in terms of politics. So, yeah, we've always kind
Speaker 2: of been in and we've always been around governments, and
Speaker 2: actually still this point you haven't actually directly partnered with
Speaker 2: the government. But it felt really gratifying to feel like
Speaker 2: you are helping that dynamic work better.
Speaker 1: What do you still have to achieve a Jigsaw and
Speaker 1: what kind of future signature projects can you tease for
Speaker 1: us Today.
Speaker 2: We're noticing a change in the culture at Jigsaw internally
Speaker 2: that actually mirrors what's happening externally. That things are moving
Speaker 2: so fast, like the technology is moving so fast, and
Speaker 2: we are inside this incredible tech company, and so I
Speaker 2: think we sometimes maybe things appear to us before they
Speaker 2: mate to others. I think we could help a lot
Speaker 2: by kind of sharing as we're going. So I think
Speaker 2: the change in the coming year that I'd like to
Speaker 2: see is more rapid iteration and more, sharing more frequently,
Speaker 2: and being more public about what we're doing.
Speaker 1: Well, you know where to come. Thank you, Thank you
Speaker 1: us for tech Stuff. I'm oz Vaaloshian. This episode was
Speaker 1: produced by Eliza Dennis and Adriana Topia. It was executive
Speaker 1: produced by me Karaen Price and Kate Osborne for Kaleidoscope
Speaker 1: and Katrina Norvel for iHeart Podcasts. Jack Insley mixed this episode,
Speaker 1: and Kyle Murdoch Rodelphime Song join us on Friday for
Speaker 1: the Week in Tech, when we'll run through the tech
Speaker 1: headlines you may have missed. Please rate, review, and reach
Speaker 1: out to us at tech Stuff podcast at gmail dot com.