WEBVTT - The Story: Big Tech, Small Town w/ Yasmin Green 

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<v Speaker 1>Welcome to tech stuff. This is the story today. A

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<v Speaker 1>conversation with Yasmin Green, the CEO of Jigsaw. Jigsaw is

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<v Speaker 1>a unit within Google that focuses on finding ways to

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<v Speaker 1>harness technology as a solution for societal issues at a

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<v Speaker 1>global and local scale. The Jigsaw team doesn't have any

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<v Speaker 1>revenue goals, and Google grants it the time and resources

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<v Speaker 1>to study intricate problems around the world, from scient extremism

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<v Speaker 1>to political censorship, to determine why it's happening and what

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<v Speaker 1>could be done about it, all from a technology and

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<v Speaker 1>solution oriented point of view. Jigsaw's most recent project took

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<v Speaker 1>Yasmin and her team to a small city in Kentucky

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<v Speaker 1>called Bowling Green. The challenge was to use AI tools

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<v Speaker 1>to create a town hall meeting for the technological age,

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<v Speaker 1>civic engagement that can be seen, heard, and considered all

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<v Speaker 1>beyond the walls of city hall. I've only has been

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<v Speaker 1>green for many years, and in an age where big

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<v Speaker 1>tech consistently drives negative headlines, I wanted to hear from

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<v Speaker 1>someone on the inside working to find best case scenarios

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<v Speaker 1>for the deluge of new tools and technologies that we're

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<v Speaker 1>now contending with. We sat down to talk about Bowling

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<v Speaker 1>Green and Jigsaw's past and future ambitions. He has been Green.

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<v Speaker 1>Welcome back to Well, last time we spoke was on Sleepwalkers.

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<v Speaker 1>This is tech stuff, but welcome back to the studio.

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<v Speaker 2>Follow you around from podcast to podcasts.

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<v Speaker 1>I think it's the other way around. Tell me a

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<v Speaker 1>bit about Jigsaw and your mission there.

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<v Speaker 2>Yeah, Digsaw is an incubator inside Google. We developed technology

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<v Speaker 2>to give people voice and choice in the world around them.

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<v Speaker 2>And I have the privilege of being the CEO.

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<v Speaker 1>And last time we met you at the director of research,

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<v Speaker 1>and now you're the CEO.

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<v Speaker 2>I have broad a shoulders. Now my voice is a

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<v Speaker 2>little lower.

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<v Speaker 1>But what have you brought to the new role? Like,

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<v Speaker 1>how have you put your stamp on the organization?

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<v Speaker 2>So I came to New York actually fourteen years ago

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<v Speaker 2>to help start Jigsaw. So I feel like I've been

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<v Speaker 2>while I bleed Jigsaw. I've been with jigsaf from the

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<v Speaker 2>beginning and then three years ago I took over as

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<v Speaker 2>CEO and it just coincided with a time of incredible

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<v Speaker 2>change and volatility. But one of the things is the

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<v Speaker 2>kind of mainstreaming of conversational AI and both I think

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<v Speaker 2>the power of the air models and also the kind

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<v Speaker 2>of public discourse and public awareness about AI, and that

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<v Speaker 2>really changed. I think also the tempo inside all of

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<v Speaker 2>the tech companies around releasing and iterating and you know,

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<v Speaker 2>the near term gain is really clear, and then maybe

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<v Speaker 2>the long term pain is less in focus. But that's

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<v Speaker 2>kind of what we think we exist to do. A

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<v Speaker 2>jigsaw is be where the major tech companies and Google

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<v Speaker 2>aren't what our kind of highest and best use would

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<v Speaker 2>be are the things that other people aren't doing right now.

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<v Speaker 1>Last time we met, we talked about a program you

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<v Speaker 1>ran to serve targeted ads to people considering the path

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<v Speaker 1>of terrorism.

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<v Speaker 2>Right, yeah. Yeah. When we started working on radicalization, which

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<v Speaker 2>was one of our first focus areas over a decade ago,

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<v Speaker 2>the idea that the Internet would have anything to do

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<v Speaker 2>with why anyone would go and join the majorhaden in

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<v Speaker 2>Afghanistan was so unfathomable, both to people who are experts

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<v Speaker 2>about you know, Islamism and the people who are experts

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<v Speaker 2>in the Internet. Now, those you know, areas of kind

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<v Speaker 2>of extremism and hate, and you know, those things are

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<v Speaker 2>they are not solved problems, but they're also not in

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<v Speaker 2>anyone's behind spots like they are very crowded that the

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<v Speaker 2>subject of regulation, et cetera. And now we're much more

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<v Speaker 2>interested in, like, what if this all goes right and

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<v Speaker 2>we do build this incredibly powerful technology and it's prevalent,

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<v Speaker 2>how do we make sure that people have a voice

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<v Speaker 2>and choice in the world around them, because it's not

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<v Speaker 2>inevitable that that will come along with the intelligence.

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<v Speaker 1>How did your sort of personal experience prepare you for

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<v Speaker 1>this role? I know you worked to Google beforehand, I

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<v Speaker 1>think in sales in Middle East and Africa and Europe,

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<v Speaker 1>and I know that you left Iran I think as

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<v Speaker 1>a young child as well. I mean, how how has

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<v Speaker 1>your kind of personal experience contributed to you being the

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<v Speaker 1>technologist that you are today based.

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<v Speaker 2>In Paris for Google, managing strategy and ops for the

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<v Speaker 2>sales teams. And I got, of course, thing, do you

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<v Speaker 2>want to come to New York and help start Jigsaw?

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<v Speaker 2>And the original frame of Jigsaw was actually kind of

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<v Speaker 2>looking at geopolitical threats. The revolution that happened in Iran

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<v Speaker 2>which led to me leaving, where the religious extremists took over.

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<v Speaker 2>They were those were violent extremists, you know, and sometimes

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<v Speaker 2>they do they make big moves and they take over land,

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<v Speaker 2>and they can you know, oppress people. For such a

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<v Speaker 2>long time, we had this view that was prevalent in

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<v Speaker 2>tech then, which was, you know, the Internet will be

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<v Speaker 2>a democratizing technology. I don't have to worry it's going

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<v Speaker 2>to be bumpy along the way, but like, one thing

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<v Speaker 2>you can be sure is this is going to bring

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<v Speaker 2>people together and connect them to information. And I was like, yeah,

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<v Speaker 2>I think there are a lot of people going to

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<v Speaker 2>have their own idea about that, and so I wanted

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<v Speaker 2>to work on things to do with the Internet that

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<v Speaker 2>were very kind of discerning about what might happen. So

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<v Speaker 2>I think this idea of alays looking around the corner

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<v Speaker 2>and saying where's where's crowded now and where people aren't looking,

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<v Speaker 2>and having the luxury to kind of have this incredible

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<v Speaker 2>group of people be thinking about what's going to happen next,

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<v Speaker 2>and you know, having the resources of Google to try

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<v Speaker 2>to affect that.

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<v Speaker 1>Jigsaw is a division of Google with I don't think

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<v Speaker 1>has any revenue. Doesn't have a revenue line, right, It's a.

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<v Speaker 2>No, it does not.

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<v Speaker 1>And Google's now in this moment where I think we'll

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<v Speaker 1>have a lot more questions about the larger role of

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<v Speaker 1>technology companies in our society. But then also the business

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<v Speaker 1>model with search and with the rise of AI is

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<v Speaker 1>more threatened. And so how does an organization like the

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<v Speaker 1>one you lead deal with those two things? On the

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<v Speaker 1>one hand, maybe being odds or surfacing things at the

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<v Speaker 1>parent company doesn't want to be surfaced. On the other hand,

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<v Speaker 1>operating in a more economically constrain the environment where there

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<v Speaker 1>are cuts and buyouts and stuff going on. How do

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<v Speaker 1>those two things affect you up day to day.

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<v Speaker 2>I mean, there are so many parts of all the

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<v Speaker 2>tech companies that work on the things that are challenges.

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<v Speaker 2>You know, they will have these massive trust and safety

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<v Speaker 2>machinery that are actually looking all the time and like

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<v Speaker 2>a trillion pieces of content and everything from child sexual

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<v Speaker 2>assault imagery to the you know, the worst of it

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<v Speaker 2>and scams. So the tech companies are full of people

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<v Speaker 2>who are looking at where things aren't going well and

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<v Speaker 2>trying to affect them. I think maybe the thing that's

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<v Speaker 2>different is that Jigsaw is providing a public commentary and

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<v Speaker 2>there I think it probably helps that we have our

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<v Speaker 2>own brand, and so we go off and do our things,

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<v Speaker 2>and sometimes sometimes we try things that don't work, and

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<v Speaker 2>in which case Google's not too bollied. And then when

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<v Speaker 2>we do things that you really are very significant contributions

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<v Speaker 2>to helping make people better off with the Internet and AI,

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<v Speaker 2>then Google's like, this is Google Jigsaw everyone, we take

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<v Speaker 2>full credit. So it seems to be a good kind

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<v Speaker 2>of arrangement that really works for Google. But you're right

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<v Speaker 2>that we often are in that we are looking at

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<v Speaker 2>like what what is not necessarily going to go? Well,

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<v Speaker 2>it is in Google's long term interest to have a

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<v Speaker 2>group that does that and that makes significant progress that

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<v Speaker 2>they can rightfully take credit for.

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<v Speaker 1>You're sort of in a sense you're not red teaming

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<v Speaker 1>particular products, but you're read teaming the cult that the

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<v Speaker 1>wider tech industry.

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<v Speaker 2>Yeah, and e specifically with the view to like, what

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<v Speaker 2>are we going to do about it? Yeah. I assure

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<v Speaker 2>you that there are many many teams that are red

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<v Speaker 2>teaming internally. They just don't come and talk to you

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<v Speaker 2>about it, or maybe they can maybe actually got an

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<v Speaker 2>advocate work. It was so great. That was really great.

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<v Speaker 1>Can you describe for our audience that text stuff, what

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<v Speaker 1>ethnographic research looks like and how you used it. If

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<v Speaker 1>you're projecting Bowling Green Kentucky.

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<v Speaker 2>Yeah, depending on where you sit, you then press that

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<v Speaker 2>jigs or does it or you're kind of appalled that

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<v Speaker 2>not every part of a tech company does this. But

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<v Speaker 2>it's basically the very patient type of research that involves

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<v Speaker 2>going in and spending time with people and observing how

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<v Speaker 2>they live in their community and people are making sense

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<v Speaker 2>of using the Internet, which is the part we care

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<v Speaker 2>about as part of an existence that extends beyond technology obviously,

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<v Speaker 2>like who they are, what their identity is, what they do,

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<v Speaker 2>who their family is with their community is how they identify,

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<v Speaker 2>and so we bring that wide angle lens to how

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<v Speaker 2>we think about challenges. So we had built this big AI,

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<v Speaker 2>really helpful tool that was used by platforms and publishers

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<v Speaker 2>around the Internet to try to have conversations that serve

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<v Speaker 2>their communities. But last year we thought to ourselves, maybe

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<v Speaker 2>we could help enable conversations, large conversations that happen not

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<v Speaker 2>just online between people, but between policymakers and their constituents.

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<v Speaker 2>And we end up doing something which we've just completed,

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<v Speaker 2>which I'm excited to talk about in Kentucky, in this

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<v Speaker 2>town called Bonling Green. But we kind of understood that

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<v Speaker 2>they wanted to have a town wide conversation, but that

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<v Speaker 2>nobody participated. There was no public input when they were

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<v Speaker 2>given space to contribute.

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<v Speaker 1>In fact, the only place you obviously a town hall

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<v Speaker 1>in America Arizona, Cinno, Fox News too use for the election, right.

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<v Speaker 1>The concept of the town hall is like more or

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<v Speaker 1>less a cliche. So how do you use technology to

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<v Speaker 1>actually have what would have the town hall in premodernaty?

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<v Speaker 2>Actually? Okay, So we went to meet with the judge

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<v Speaker 2>executive of this county. It's like the mayor of this

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<v Speaker 2>county who's called Doug Norman. Incredible, I'm gonna call him Doug.

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<v Speaker 2>This town is a doubling in size over the next

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<v Speaker 2>twenty years. It's going from one hundred and forty eight manation.

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<v Speaker 2>Might not be getting those rights those numbers right, it

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<v Speaker 2>might be doubling even more. But it's basically like adding

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<v Speaker 2>another bowling green to the county, which sounds great like

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<v Speaker 2>growth and jobs and development. And at the same time

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<v Speaker 2>we're a mixed bag of emotions in the community. So

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<v Speaker 2>there's a big farming constituency and they are concerned about

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<v Speaker 2>land use. In the irreversible decision to let people develop

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<v Speaker 2>on your land, lifelong residents are concerned about the preserving

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<v Speaker 2>their culture and their building, and there's a lot of

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<v Speaker 2>concern about migration. And it's interesting that in Kentucky and

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<v Speaker 2>Bowling Green the concern is not about migration from foreigners

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<v Speaker 2>but from people from Nashville, Tennessee. Really, they're like, we

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<v Speaker 2>don't want to become like Nashville extended here. So there's

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<v Speaker 2>all like valid concerns that they have, and they're going

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<v Speaker 2>to have to have all you know, all this growth.

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<v Speaker 2>And actually the words from Doug capture it best. He

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<v Speaker 2>said to them, do we want this growth to happen

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<v Speaker 2>to us or do we want the growth to happen

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<v Speaker 2>for us? If we want to chappen for us, we

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<v Speaker 2>have to come together and set a vision for Bowling Green,

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<v Speaker 2>which is really amazing. But then you are similarly perfectly,

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<v Speaker 2>how's it going like with you know, Townholls, And he's like, yeah,

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<v Speaker 2>we get about eight or nine people, and so we're like,

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<v Speaker 2>maybe we can help because we have a lot of

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<v Speaker 2>experience with tech and large girl conversations. But it always

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<v Speaker 2>starts like product them always starts with like field work.

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<v Speaker 2>So we went to Bowling Green to go and spend

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<v Speaker 2>time with people, people who are really marginalized from politics,

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<v Speaker 2>like the elder, these students, recent migrants, et cetera, people

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<v Speaker 2>who've just been left incarceration, to figure out what what

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<v Speaker 2>would stop them from participating, And so we do things

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<v Speaker 2>like we took a couple to like a tree planting meeting.

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<v Speaker 2>We took like this college graduate to a city planning

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<v Speaker 2>commission get together about whether someone should be allowed to

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<v Speaker 2>add to the back of their pub. We took all

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<v Speaker 2>of them and so you can observe what is holding

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<v Speaker 2>them back, and it was the things that they said

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<v Speaker 2>resonated so much with me. They don't think that there

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<v Speaker 2>they have the expertise, they don't think they matter, they

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<v Speaker 2>don't think that if they use their voice it would

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<v Speaker 2>be listened to. And it's all stuff that who hasn't

0:11:34.320 --> 0:11:36.480
<v Speaker 2>been in like a large meeting where you're like, I

0:11:36.480 --> 0:11:39.640
<v Speaker 2>don't really understand what we're talking about, or I don't

0:11:40.160 --> 0:11:42.160
<v Speaker 2>understand my role in this discussion, or like if I

0:11:42.240 --> 0:11:44.199
<v Speaker 2>use my voice, you know my boss going to hear me,

0:11:44.280 --> 0:11:45.960
<v Speaker 2>or is it going to go anywhere? So if we

0:11:45.960 --> 0:11:48.400
<v Speaker 2>can relate to it, they get that level. The fact

0:11:48.400 --> 0:11:51.319
<v Speaker 2>that people really show up to their local community, whether

0:11:51.360 --> 0:11:55.000
<v Speaker 2>it's like their refugee community or their LGBTQ community, or

0:11:55.040 --> 0:11:58.080
<v Speaker 2>the sports club or the church. But then when you

0:11:58.240 --> 0:12:01.560
<v Speaker 2>like come to the town conversation, the civic oh, I

0:12:01.559 --> 0:12:03.920
<v Speaker 2>don't know, I don't look or sound the part. You know,

0:12:04.040 --> 0:12:06.839
<v Speaker 2>the college student we took to the city planning commission thing,

0:12:06.840 --> 0:12:08.720
<v Speaker 2>he was like, I can't go to that. I'm not

0:12:08.760 --> 0:12:11.080
<v Speaker 2>a lawyer. And of course when he went there were

0:12:11.080 --> 0:12:13.480
<v Speaker 2>no lawyers. But like, that's what we have in our head,

0:12:13.840 --> 0:12:15.600
<v Speaker 2>is like, I am the expertise.

0:12:16.080 --> 0:12:18.720
<v Speaker 1>Can you tell me a little bit more about perspective API.

0:12:19.320 --> 0:12:21.640
<v Speaker 1>I know it's developed by Jigsaw and it's now used

0:12:21.640 --> 0:12:25.400
<v Speaker 1>by Wikipedia and Wall Street Journal, And essentially what it

0:12:25.440 --> 0:12:31.240
<v Speaker 1>does is regulate the comments sections using AI to understand

0:12:31.240 --> 0:12:34.040
<v Speaker 1>the impact any given comment might have on other commenters

0:12:34.080 --> 0:12:39.040
<v Speaker 1>or participants and score that impact. But I'm curious, is

0:12:39.080 --> 0:12:43.000
<v Speaker 1>the Bowling Green Project an extension of Jigsaw's work on perspective.

0:12:43.520 --> 0:12:45.760
<v Speaker 2>It's hard to even remember that, you know, nine or

0:12:45.760 --> 0:12:48.960
<v Speaker 2>ten years ago, all these publishers are closing their comment

0:12:49.000 --> 0:12:52.080
<v Speaker 2>spaces because they couldn't even have a civil conversation, and

0:12:52.120 --> 0:12:54.760
<v Speaker 2>so Perspective was a you know, an AI that helps

0:12:54.840 --> 0:12:58.720
<v Speaker 2>with managing online conversations. They get ranks all different types

0:12:58.760 --> 0:13:02.200
<v Speaker 2>of things, everything from like ad hominem attacks too, is

0:13:02.240 --> 0:13:05.640
<v Speaker 2>something constructive and what explained like it scores them for

0:13:05.840 --> 0:13:08.040
<v Speaker 2>publishers to decide what kind of conversation you want to have.

0:13:08.600 --> 0:13:11.400
<v Speaker 2>But in the case of policymakers in there, you know

0:13:11.440 --> 0:13:13.760
<v Speaker 2>the people they represent, they need to be in conversation too.

0:13:13.800 --> 0:13:17.160
<v Speaker 2>There's this quote that I love, which is that conversation

0:13:17.320 --> 0:13:20.760
<v Speaker 2>is the soul of democracy. What gives democracies legitimacy is

0:13:20.960 --> 0:13:23.360
<v Speaker 2>our ability to have a free and open exchange of ideas.

0:13:24.080 --> 0:13:27.640
<v Speaker 2>And so the thing that we wanted to help Doug with,

0:13:28.480 --> 0:13:30.559
<v Speaker 2>which kind of informed what we developed, which we call

0:13:30.800 --> 0:13:34.320
<v Speaker 2>sense making, was he wants to tell his people come

0:13:34.400 --> 0:13:36.959
<v Speaker 2>and be in a conversation with me. People weren't showing up.

0:13:37.120 --> 0:13:40.200
<v Speaker 1>So it wasn't like there's an existing conversation happening. How

0:13:40.280 --> 0:13:42.880
<v Speaker 1>do we make it more provatal inclusive? It was how

0:13:42.920 --> 0:13:46.200
<v Speaker 1>do we create the environment for a conversation to happen?

0:13:46.360 --> 0:13:48.880
<v Speaker 2>Yes, and they need to believe that he will hear

0:13:48.920 --> 0:13:52.680
<v Speaker 2>their voice if they express it. People who study this say,

0:13:52.679 --> 0:13:54.920
<v Speaker 2>if there's nothing that's worse than not asking people for

0:13:54.960 --> 0:13:55.840
<v Speaker 2>their opinion.

0:13:55.559 --> 0:13:59.520
<v Speaker 1>Asking people ignoring them because that's truly demotivating.

0:14:00.920 --> 0:14:03.480
<v Speaker 2>So we realized that he would need to be able

0:14:03.480 --> 0:14:05.920
<v Speaker 2>to tell them that their voice would be heard, and

0:14:06.000 --> 0:14:09.240
<v Speaker 2>so we worked. We ended up working with a platform

0:14:09.320 --> 0:14:13.040
<v Speaker 2>called polist to solicit input and it was just open ended,

0:14:13.440 --> 0:14:15.520
<v Speaker 2>open ended like answers, so it was really in your

0:14:15.520 --> 0:14:18.520
<v Speaker 2>own words and on your own terms, So people were

0:14:18.520 --> 0:14:19.840
<v Speaker 2>free to say what they wanted. They're free to do

0:14:19.880 --> 0:14:22.320
<v Speaker 2>it at their own time, in their bedroom, you know,

0:14:22.400 --> 0:14:25.400
<v Speaker 2>an anonymously. So it was really like a lot of

0:14:25.440 --> 0:14:27.880
<v Speaker 2>what we heard in the ethnography we were trying to

0:14:27.920 --> 0:14:30.640
<v Speaker 2>account for. My team sat down with two sisters that

0:14:30.680 --> 0:14:34.320
<v Speaker 2>were recently relocated Bowling Green from Afghanistan, and they went

0:14:34.440 --> 0:14:36.840
<v Speaker 2>and in their home like with like over tree. Then

0:14:36.920 --> 0:14:38.760
<v Speaker 2>they were like you know, with the headscarves everything. They

0:14:38.760 --> 0:14:40.200
<v Speaker 2>were asking them like what what would it take for

0:14:40.240 --> 0:14:42.400
<v Speaker 2>you to come and be part of this conversation And

0:14:42.440 --> 0:14:44.280
<v Speaker 2>they were just like that, they had no idea what

0:14:44.320 --> 0:14:47.800
<v Speaker 2>civic participation referred to, the idea of like even attending

0:14:47.920 --> 0:14:51.480
<v Speaker 2>a town hall, never mind being asked to speak, was

0:14:51.640 --> 0:14:54.800
<v Speaker 2>a complete anathema to them. So this conversation that we

0:14:54.840 --> 0:14:57.280
<v Speaker 2>set up, which is called what could Bowling Green be,

0:14:57.320 --> 0:14:59.480
<v Speaker 2>which we did with this incredible partner on the ground

0:14:59.480 --> 0:15:02.800
<v Speaker 2>called Innovation an Engine. The conversation was like designed to

0:15:02.800 --> 0:15:05.000
<v Speaker 2>be like you are free to say what you want,

0:15:05.200 --> 0:15:07.600
<v Speaker 2>where you want, whenever you want. And it was a

0:15:07.600 --> 0:15:10.080
<v Speaker 2>month long conversation and we did it through this POSE

0:15:10.160 --> 0:15:13.280
<v Speaker 2>platform and the idea was speak and you'll be heard.

0:15:13.800 --> 0:15:17.280
<v Speaker 2>And the part that we used AI for was making

0:15:17.320 --> 0:15:20.080
<v Speaker 2>sense of what everybody said in a way that could

0:15:20.120 --> 0:15:22.040
<v Speaker 2>be shared back to them, that they would see their

0:15:22.080 --> 0:15:24.680
<v Speaker 2>voices in the conversation and could be shared back with

0:15:24.720 --> 0:15:26.840
<v Speaker 2>the policymaker so he could take an action.

0:15:37.720 --> 0:15:41.240
<v Speaker 1>After the break, the largest town hall in US history

0:15:41.600 --> 0:15:55.600
<v Speaker 1>and how Jigsaw made it happen. Welcome back to tech stuff.

0:15:55.920 --> 0:15:58.960
<v Speaker 1>Before the break, we were talking with Yasmin Green, CEO

0:15:59.040 --> 0:16:02.720
<v Speaker 1>of Jigsaw, about how the initial pieces of an ambitious

0:16:02.720 --> 0:16:06.080
<v Speaker 1>project using AI to create a more inclusive and active

0:16:06.120 --> 0:16:11.200
<v Speaker 1>town hall came together in the town of Bowling Green, Kentucky. So,

0:16:11.440 --> 0:16:13.720
<v Speaker 1>taking a few steps back, how does this come about? Like,

0:16:13.760 --> 0:16:16.480
<v Speaker 1>how do you choose a community to partner with? And

0:16:16.720 --> 0:16:18.400
<v Speaker 1>I guess what are the stakes for you to be

0:16:18.440 --> 0:16:22.200
<v Speaker 1>working with government versus just in a purely online arena,

0:16:22.320 --> 0:16:25.000
<v Speaker 1>Like how much pressure and also opportunities.

0:16:24.480 --> 0:16:27.560
<v Speaker 2>Does that add? Kentucky's actually a really pioneering state, and

0:16:27.760 --> 0:16:29.040
<v Speaker 2>partly a little bit by it because now I spent

0:16:29.200 --> 0:16:31.160
<v Speaker 2>much time there, but the cross even not in Bonling Green,

0:16:31.240 --> 0:16:33.840
<v Speaker 2>other places do. They're doing a lot of civic tech

0:16:33.880 --> 0:16:36.320
<v Speaker 2>work and they're very forward. And Bowling Green in particular

0:16:36.440 --> 0:16:39.320
<v Speaker 2>was known because they had done something several years ago

0:16:39.320 --> 0:16:42.480
<v Speaker 2>where they had done public participation with the local newspaper. Okay,

0:16:42.960 --> 0:16:44.720
<v Speaker 2>and we had heard that they're putting together the stake

0:16:44.880 --> 0:16:46.360
<v Speaker 2>vision for twenty fifty.

0:16:46.440 --> 0:16:48.320
<v Speaker 1>So what do we want Bowling Green? Yeah, so you'd

0:16:48.360 --> 0:16:50.280
<v Speaker 1>heard about this, you knew that they would take forward.

0:16:50.720 --> 0:16:52.520
<v Speaker 2>Yes, and we're like, maybe we can actually they come

0:16:52.560 --> 0:16:54.680
<v Speaker 2>and give them something really useful. Maybe we can co

0:16:54.720 --> 0:16:56.920
<v Speaker 2>develop something with them. And so they said, okay, let's

0:16:56.920 --> 0:16:58.680
<v Speaker 2>do this month and conversation to hear what people have

0:16:58.760 --> 0:17:02.160
<v Speaker 2>to say. And then they actually did like super local

0:17:02.640 --> 0:17:04.680
<v Speaker 2>like ground game marketing campaign.

0:17:04.320 --> 0:17:06.120
<v Speaker 1>Where you involved with a marketing campaign or.

0:17:06.200 --> 0:17:08.399
<v Speaker 2>Yeah, we partnered with them, but it was like local

0:17:08.400 --> 0:17:10.040
<v Speaker 2>creative agencies that came up with the creators.

0:17:10.040 --> 0:17:12.919
<v Speaker 1>So basically get people to be aware and think it

0:17:12.960 --> 0:17:15.120
<v Speaker 1>was fun, evangelize interact with the platform.

0:17:15.320 --> 0:17:17.959
<v Speaker 2>Yeah, yeah, and exactly, And the main thing was just

0:17:18.000 --> 0:17:21.960
<v Speaker 2>to be local. So things were translated into nine languages.

0:17:22.280 --> 0:17:24.359
<v Speaker 2>They they put them in you know, they fly at

0:17:24.359 --> 0:17:27.280
<v Speaker 2>international supermarkets, they went to make a church is, they

0:17:27.280 --> 0:17:28.320
<v Speaker 2>went to basketball games.

0:17:28.359 --> 0:17:32.280
<v Speaker 1>So you created awareness and then whether like QR codes

0:17:32.359 --> 0:17:34.240
<v Speaker 1>that people like clicked on their phones and they would

0:17:34.280 --> 0:17:36.359
<v Speaker 1>take it into the conversational platform or like how what

0:17:36.440 --> 0:17:36.639
<v Speaker 1>was the.

0:17:36.800 --> 0:17:38.960
<v Speaker 2>Yeah, basically you look, yes, they're working on code. Do

0:17:39.080 --> 0:17:40.760
<v Speaker 2>you go to what could be GB? In fact, you

0:17:40.760 --> 0:17:42.680
<v Speaker 2>can stop it. What could Bowling Green be? Is the

0:17:42.680 --> 0:17:45.399
<v Speaker 2>website dot com and you can see the report. But

0:17:45.520 --> 0:17:47.919
<v Speaker 2>people would log in and then you just get like

0:17:47.960 --> 0:17:51.000
<v Speaker 2>a question prompt what do you want for Bowling Green

0:17:51.040 --> 0:17:53.320
<v Speaker 2>in the future, Which that's that depended you put your

0:17:53.359 --> 0:17:54.920
<v Speaker 2>answers in and.

0:17:55.200 --> 0:17:56.280
<v Speaker 1>Only one single prompt.

0:17:56.320 --> 0:17:58.120
<v Speaker 2>There's one question and could you could put you could

0:17:58.160 --> 0:18:00.000
<v Speaker 2>keep putting more of what you want. You probably have lots.

0:18:00.119 --> 0:18:02.760
<v Speaker 2>It's pretty open. You might have ideas for education or infrastructure,

0:18:02.960 --> 0:18:06.639
<v Speaker 2>you know, restaurants. But then there's voting, so you you

0:18:06.720 --> 0:18:09.200
<v Speaker 2>vote on other people's input. And that was a really

0:18:09.240 --> 0:18:12.560
<v Speaker 2>important signal to see like where, if anywhere, do we

0:18:12.640 --> 0:18:15.840
<v Speaker 2>agree right and where we put so much effort into

0:18:15.880 --> 0:18:18.159
<v Speaker 2>trying to make a like make this local and be

0:18:18.240 --> 0:18:21.840
<v Speaker 2>make it accessible and still like you know, the beginning

0:18:21.840 --> 0:18:23.960
<v Speaker 2>of the month long conversation, we were like, is anyone

0:18:24.920 --> 0:18:27.200
<v Speaker 2>is anyone going to turn up to this? Yeah? Because

0:18:27.200 --> 0:18:28.960
<v Speaker 2>maybe we do all this effort and it's eight people.

0:18:29.000 --> 0:18:30.040
<v Speaker 1>Did you show a tumble.

0:18:31.560 --> 0:18:37.000
<v Speaker 2>Right? Right? So the baseline's eight okay, So we were like,

0:18:37.880 --> 0:18:39.919
<v Speaker 2>thirty will be good. Thirty will be good. Now, so

0:18:41.240 --> 0:18:42.600
<v Speaker 2>we're like, if we could get a one thousand or

0:18:42.640 --> 0:18:44.920
<v Speaker 2>two thousand, and so it was eight.

0:18:44.840 --> 0:18:47.360
<v Speaker 1>Thousand, eight thousand respondents.

0:18:46.960 --> 0:18:49.159
<v Speaker 2>This kind of like AI enabled town hall was a

0:18:49.240 --> 0:18:53.200
<v Speaker 2>thousand times larger than the regular town hall, and one

0:18:53.240 --> 0:18:55.640
<v Speaker 2>in every ten residents basically the equivalent of one every

0:18:55.640 --> 0:18:59.000
<v Speaker 2>ten residents participated. So as far as like that's what

0:18:59.200 --> 0:19:01.280
<v Speaker 2>the folks on the ground and say, is that it

0:19:01.320 --> 0:19:04.439
<v Speaker 2>meant that people were discussing this topic even outside of

0:19:04.640 --> 0:19:07.080
<v Speaker 2>the actual platform because there was that much participation, So

0:19:07.119 --> 0:19:09.280
<v Speaker 2>it kind of pas the tipping point. So it was

0:19:09.280 --> 0:19:10.679
<v Speaker 2>really nice to see the numbers go up. They did

0:19:10.720 --> 0:19:12.360
<v Speaker 2>go up, they did spike a bit towards the end,

0:19:12.800 --> 0:19:17.320
<v Speaker 2>and it was actually the largest digital town hall in

0:19:17.600 --> 0:19:20.560
<v Speaker 2>US history. So it's the most that anyone's come together,

0:19:20.600 --> 0:19:23.399
<v Speaker 2>and that's US partnering with a town in Kentucky. So

0:19:23.400 --> 0:19:25.560
<v Speaker 2>if you like imagine what is possible at the state

0:19:25.640 --> 0:19:28.399
<v Speaker 2>level or across states if you bring people together to

0:19:28.400 --> 0:19:31.879
<v Speaker 2>share their view. Although there was before like advocating for

0:19:32.000 --> 0:19:35.040
<v Speaker 2>more of those type of CIF conversations, I think the

0:19:35.040 --> 0:19:37.399
<v Speaker 2>first big apprehension was is anyone could show up? Ye?

0:19:38.280 --> 0:19:41.479
<v Speaker 2>The second was, well, you know, if this is a

0:19:41.520 --> 0:19:45.040
<v Speaker 2>scaled version of an in person town hall, we might

0:19:45.119 --> 0:19:47.159
<v Speaker 2>kind of be in trouble because the eight people that

0:19:47.240 --> 0:19:51.639
<v Speaker 2>come to an in person town hall detractors detracted typically, right,

0:19:51.840 --> 0:19:54.120
<v Speaker 2>the ambivalent people don't go and show up, or people

0:19:54.119 --> 0:19:55.640
<v Speaker 2>who are like quite like the idea and also could

0:19:55.640 --> 0:19:57.399
<v Speaker 2>see why not the idea? That's not the people that

0:19:57.440 --> 0:19:59.560
<v Speaker 2>get you motivated to get off the counch and drive

0:19:59.600 --> 0:20:01.560
<v Speaker 2>all the way and wait there. It's usually people who

0:20:01.880 --> 0:20:03.960
<v Speaker 2>really don't want the thing to happen. So I think

0:20:04.000 --> 0:20:05.679
<v Speaker 2>local leaders were like, well, we're just going to get

0:20:05.680 --> 0:20:08.440
<v Speaker 2>eight thousand of them entrance, you know, because they don't

0:20:08.440 --> 0:20:10.680
<v Speaker 2>want to scale in that sense, And that was why

0:20:10.720 --> 0:20:13.240
<v Speaker 2>it's so important to do like such a broad marketing

0:20:13.240 --> 0:20:16.800
<v Speaker 2>campaign to like all these different constituencies, but the bigger

0:20:16.920 --> 0:20:19.800
<v Speaker 2>side of relief from local leaders when they looked at

0:20:20.160 --> 0:20:23.000
<v Speaker 2>their feedback and a lot of stuff they anticipated. There

0:20:23.080 --> 0:20:25.080
<v Speaker 2>was like in a growing town, you need more infrastructure,

0:20:25.080 --> 0:20:28.520
<v Speaker 2>you need more roads. Lots of really interesting thoughts on education,

0:20:28.840 --> 0:20:30.919
<v Speaker 2>on like restaurants. There was just one submission on the

0:20:30.960 --> 0:20:35.920
<v Speaker 2>restaurant and interesting kind of like unexpected proposals on a

0:20:36.000 --> 0:20:37.840
<v Speaker 2>number of fronts, but the one that makes me giggles

0:20:38.200 --> 0:20:41.399
<v Speaker 2>in the area of like food and restaurants. One person

0:20:41.480 --> 0:20:45.320
<v Speaker 2>submitted an idea to pass a law to mandate restaurants

0:20:45.359 --> 0:20:48.600
<v Speaker 2>to include ketchup saches in the takeaway like the takeaway

0:20:48.640 --> 0:20:51.480
<v Speaker 2>bags of like fast food restaurants. So you know, that's

0:20:51.480 --> 0:20:53.320
<v Speaker 2>how you know that people are really expressing their views.

0:20:53.320 --> 0:20:56.480
<v Speaker 2>And we've got good mixed views but didn't have much support.

0:20:56.840 --> 0:20:59.119
<v Speaker 1>How did people express support for one another? Like I

0:20:59.160 --> 0:21:01.159
<v Speaker 1>go on the platform, answer this is you know, I

0:21:01.160 --> 0:21:05.240
<v Speaker 1>want more free ketchup saches? Is that that Now I

0:21:05.240 --> 0:21:07.280
<v Speaker 1>don't live in Boling. I read that it was Geo

0:21:07.480 --> 0:21:10.879
<v Speaker 1>Geo fence. Otherwise it would have been me. But then

0:21:11.280 --> 0:21:14.400
<v Speaker 1>is that then like available for other people to comment on? Okay,

0:21:14.600 --> 0:21:16.479
<v Speaker 1>so it's like a message boarders, it's not just a one.

0:21:16.600 --> 0:21:18.520
<v Speaker 2>So they're routing so you you put your idea and

0:21:18.560 --> 0:21:21.000
<v Speaker 2>then they're routing ideas for you to vote on. And

0:21:21.040 --> 0:21:23.720
<v Speaker 2>it's actually much You get a much higher percentage of

0:21:23.920 --> 0:21:27.600
<v Speaker 2>people like the The votes are much higher than the

0:21:27.960 --> 0:21:30.800
<v Speaker 2>actual submissions. Yeah, because it's much easier to vote and

0:21:30.800 --> 0:21:32.360
<v Speaker 2>you're kind of in there and it's a little addictive.

0:21:32.680 --> 0:21:35.280
<v Speaker 2>And I think that's one of the really really cool

0:21:35.320 --> 0:21:38.960
<v Speaker 2>things about this conversation is that it was designed to

0:21:39.040 --> 0:21:42.800
<v Speaker 2>advance a very large conversation. It wasn't designed to have

0:21:42.840 --> 0:21:44.679
<v Speaker 2>like eight thousand and one on ones. That's not what

0:21:44.680 --> 0:21:45.919
<v Speaker 2>we're trying to do when we're trying to have a

0:21:45.920 --> 0:21:49.840
<v Speaker 2>long conversation. And that that's interesting because there isn't the

0:21:49.880 --> 0:21:52.880
<v Speaker 2>option to reply the way that purchases platform is set up.

0:21:53.200 --> 0:21:54.840
<v Speaker 2>You have an idea, and you can vote on other

0:21:54.840 --> 0:21:57.240
<v Speaker 2>people's ideas, and if you really don't like the catch

0:21:57.280 --> 0:21:59.119
<v Speaker 2>up idea and you actually think it should be mustered,

0:21:59.240 --> 0:22:01.119
<v Speaker 2>put your idea in the area. Yeah, but we're not

0:22:01.160 --> 0:22:02.520
<v Speaker 2>you're not telling you know, you're not going to get

0:22:02.520 --> 0:22:05.400
<v Speaker 2>into some you know, back and forth with the person

0:22:05.480 --> 0:22:08.840
<v Speaker 2>who suggested Ki jump is you know, from the wrong

0:22:08.880 --> 0:22:10.680
<v Speaker 2>part of town, and everyone's asking that part of town.

0:22:10.680 --> 0:22:12.639
<v Speaker 2>You know, here's no point going down that you know,

0:22:13.080 --> 0:22:15.480
<v Speaker 2>spiral of the ad hominym attacks, et cetera. That happens

0:22:15.480 --> 0:22:18.040
<v Speaker 2>a not on social media. So the conversations dis like

0:22:18.200 --> 0:22:21.399
<v Speaker 2>designed differently from the get go that you put your ideas,

0:22:21.400 --> 0:22:23.120
<v Speaker 2>you vote, if you want to keep going after you've

0:22:23.160 --> 0:22:24.960
<v Speaker 2>given your thumbs up your thumbs down, you're welcome to

0:22:24.960 --> 0:22:27.199
<v Speaker 2>put your idea in there. And it well. So then

0:22:27.200 --> 0:22:28.760
<v Speaker 2>when we had to take stock of like, well did

0:22:28.800 --> 0:22:32.800
<v Speaker 2>any of these ideas they resonate with other people? So

0:22:32.840 --> 0:22:36.040
<v Speaker 2>there were four thousand policy proposals and over half of

0:22:36.080 --> 0:22:38.960
<v Speaker 2>them had near universal support.

0:22:38.840 --> 0:22:42.320
<v Speaker 1>Near universal support over half. So in other words, what

0:22:42.359 --> 0:22:45.560
<v Speaker 1>you take from it is the community knows what's good

0:22:45.560 --> 0:22:45.800
<v Speaker 1>for it.

0:22:46.280 --> 0:22:48.960
<v Speaker 2>Well yeah, and and that you know, there are so

0:22:49.040 --> 0:22:51.160
<v Speaker 2>many opportunities for us to hear from the people who

0:22:51.200 --> 0:22:53.920
<v Speaker 2>are the angriest or at least well informed.

0:22:54.040 --> 0:22:56.720
<v Speaker 1>Or did you know the voter registration of the participants

0:22:56.800 --> 0:22:57.280
<v Speaker 1>or I mean.

0:22:57.280 --> 0:23:00.480
<v Speaker 2>Was this no, but it's it's it is a it's

0:23:00.480 --> 0:23:03.960
<v Speaker 2>a purpolish place, so it's quite a mix. And you

0:23:04.000 --> 0:23:06.760
<v Speaker 2>saw some of the things that you see you know, nationally,

0:23:07.440 --> 0:23:09.560
<v Speaker 2>but most of the things that local people care about

0:23:09.600 --> 0:23:11.520
<v Speaker 2>are you know, what is going to happen to their

0:23:11.600 --> 0:23:14.560
<v Speaker 2>K through twelve education. It turns out they want more

0:23:14.640 --> 0:23:17.120
<v Speaker 2>vocational skills, you know, like they want their university, which

0:23:17.160 --> 0:23:19.240
<v Speaker 2>is the pride of like you know, Western Kentucky University,

0:23:19.240 --> 0:23:21.920
<v Speaker 2>they want it to be more integrated into their economy

0:23:21.960 --> 0:23:24.280
<v Speaker 2>and their like workforce development. And there's a lot of

0:23:24.320 --> 0:23:26.560
<v Speaker 2>things that they want. And the perspective of not just

0:23:26.640 --> 0:23:30.679
<v Speaker 2>dog actually but even like private section leaders was like,

0:23:31.000 --> 0:23:34.080
<v Speaker 2>that's great. Now we have now we can go and

0:23:34.200 --> 0:23:37.600
<v Speaker 2>lead and build and make changes knowing that we have

0:23:37.680 --> 0:23:41.480
<v Speaker 2>broad public support. Otherwise, if you're just based basing you know,

0:23:41.560 --> 0:23:46.200
<v Speaker 2>your like sentiment gauging on public calls and social media,

0:23:47.040 --> 0:23:48.879
<v Speaker 2>just thinking that like no one wants anything to happen,

0:23:48.960 --> 0:23:51.200
<v Speaker 2>you know, or that like you might have some supporters,

0:23:51.240 --> 0:23:53.879
<v Speaker 2>but also like everyone else is an avid, you know,

0:23:54.160 --> 0:23:57.200
<v Speaker 2>avid distractor. So so I think those are two really

0:23:57.240 --> 0:24:00.440
<v Speaker 2>important essons. One people really will show up, they really

0:24:00.480 --> 0:24:03.160
<v Speaker 2>do want to shape their future, you know, And two

0:24:03.200 --> 0:24:06.159
<v Speaker 2>that there is more that unites us then divides us,

0:24:06.160 --> 0:24:09.399
<v Speaker 2>which is kind of imperative for us to keep you know,

0:24:09.640 --> 0:24:12.359
<v Speaker 2>in our minds and cultivate at this moment.

0:24:12.920 --> 0:24:17.800
<v Speaker 1>And what was Jigsaw's role or technology deployment because the

0:24:17.880 --> 0:24:21.320
<v Speaker 1>marketing campaign was like panel by local agencies, the polished

0:24:21.359 --> 0:24:24.600
<v Speaker 1>platform was it's not a Jigsaw platform, right, So what

0:24:24.640 --> 0:24:27.320
<v Speaker 1>did what was the layer that you guys added It was.

0:24:27.280 --> 0:24:30.280
<v Speaker 2>The making sense of the conversation. So if you can

0:24:30.320 --> 0:24:33.760
<v Speaker 2>get lots of people to contribute, how can you guarantee

0:24:33.800 --> 0:24:36.120
<v Speaker 2>that their voice will be heard? And so it really

0:24:36.160 --> 0:24:39.800
<v Speaker 2>is using this case, Google's aigemini using an eye for

0:24:39.880 --> 0:24:42.479
<v Speaker 2>what it does best, which is they take large amounts

0:24:42.480 --> 0:24:45.479
<v Speaker 2>of information that it expressed in a very local and

0:24:45.480 --> 0:24:49.560
<v Speaker 2>personal way, understand it, organize it, they sort it, prioritize it,

0:24:49.600 --> 0:24:51.679
<v Speaker 2>and then give it back to people. So like, you

0:24:51.680 --> 0:24:55.040
<v Speaker 2>know the thing if you go to what could bowlinggreenpeed

0:24:55.080 --> 0:24:58.000
<v Speaker 2>dot com, you'll see the report and it's like, you know,

0:24:58.040 --> 0:25:00.159
<v Speaker 2>Google Maps, we think at Google mapps, you started very

0:25:00.200 --> 0:25:01.720
<v Speaker 2>high level. If you'll look at like the states of

0:25:01.920 --> 0:25:04.000
<v Speaker 2>in America, that's the first thing that you see, and

0:25:04.040 --> 0:25:05.800
<v Speaker 2>then you're like, actually, I'm kind of interested in New York.

0:25:05.840 --> 0:25:08.520
<v Speaker 2>And then you're like interest in my neighborhood and they're interesting.

0:25:08.520 --> 0:25:10.240
<v Speaker 2>You're like, oh, how can you see my front door?

0:25:10.400 --> 0:25:12.240
<v Speaker 2>You know, And that's how it is with this map

0:25:12.280 --> 0:25:15.359
<v Speaker 2>of opinions, like not geographical terrain, but opinion terrain. But

0:25:15.359 --> 0:25:16.640
<v Speaker 2>you start the top and you're like, what do people

0:25:16.640 --> 0:25:18.240
<v Speaker 2>have born and green care about? And then it has

0:25:18.240 --> 0:25:20.680
<v Speaker 2>those you know, instructural education, et cetera. And then you're

0:25:20.720 --> 0:25:22.840
<v Speaker 2>driven You're like, actually, what I really care about is

0:25:23.480 --> 0:25:25.359
<v Speaker 2>you know, education, and then go through, you know, you

0:25:25.400 --> 0:25:28.840
<v Speaker 2>just keep digging and most people, most people have specific

0:25:28.840 --> 0:25:31.600
<v Speaker 2>areas of interest. But the truth is people want to

0:25:31.640 --> 0:25:35.840
<v Speaker 2>know where they sit relative to their neighbors, and so

0:25:36.040 --> 0:25:38.200
<v Speaker 2>people want to actually find where they are. Because there's

0:25:38.200 --> 0:25:40.639
<v Speaker 2>this plot graph on there that has a distribution of

0:25:40.680 --> 0:25:42.840
<v Speaker 2>all the proposals and how much agreement they got, and

0:25:42.880 --> 0:25:44.560
<v Speaker 2>so you can see am I like on the am

0:25:44.600 --> 0:25:47.479
<v Speaker 2>I the fringe opinion holder even though like me and

0:25:47.520 --> 0:25:50.679
<v Speaker 2>all my two friends agree with me, or actually is

0:25:50.680 --> 0:25:52.800
<v Speaker 2>something that I've been scared to say out loud, something

0:25:52.840 --> 0:25:54.840
<v Speaker 2>that a lot of other people believe. So you can

0:25:54.880 --> 0:25:57.080
<v Speaker 2>go in there and see that, which is really powerful.

0:25:57.080 --> 0:26:00.159
<v Speaker 2>And of course most people agreed with most things. So

0:26:00.240 --> 0:26:02.320
<v Speaker 2>I think it does restore our sense of you know,

0:26:02.359 --> 0:26:05.160
<v Speaker 2>like it's a funhouse of mirror sometimes social media where

0:26:05.160 --> 0:26:08.119
<v Speaker 2>you're like, oh my goodness, everyone's extreme. It's just me

0:26:08.240 --> 0:26:11.240
<v Speaker 2>here that doesn't want to have like an extreme mistake

0:26:11.280 --> 0:26:11.920
<v Speaker 2>on things.

0:26:12.359 --> 0:26:15.760
<v Speaker 1>What are the key policy ideas that have emerged and

0:26:16.920 --> 0:26:19.120
<v Speaker 1>will you measure success based on whether or not they're

0:26:19.160 --> 0:26:21.040
<v Speaker 1>enacted or are you, in a sense now passing the

0:26:21.080 --> 0:26:21.640
<v Speaker 1>bat on the lull.

0:26:22.400 --> 0:26:26.160
<v Speaker 2>It's definitely in the court of their leaders, and it's

0:26:26.200 --> 0:26:28.520
<v Speaker 2>their process so to begin with. So I think they

0:26:28.640 --> 0:26:30.960
<v Speaker 2>genuinely do want people's input because the process kind of

0:26:31.000 --> 0:26:35.520
<v Speaker 2>proceeded our involvement. But one example was they had wanted

0:26:35.520 --> 0:26:38.600
<v Speaker 2>to do a riverside development. They built building up the

0:26:38.680 --> 0:26:41.320
<v Speaker 2>area along the riverside, and when they had got tried

0:26:41.320 --> 0:26:44.199
<v Speaker 2>to get public input, very few people pay and they

0:26:44.240 --> 0:26:47.440
<v Speaker 2>were detractors. But then if you look at the feedback

0:26:47.440 --> 0:26:50.120
<v Speaker 2>on that one topic in the reports, like seventy five

0:26:50.160 --> 0:26:52.120
<v Speaker 2>plus percent of people support it. So I think they're

0:26:52.119 --> 0:26:54.880
<v Speaker 2>now going to accelerate that. What we did, in terms

0:26:54.880 --> 0:26:57.119
<v Speaker 2>of like really trying to gauge people's sense of like

0:26:57.160 --> 0:27:00.359
<v Speaker 2>whether it mattered for them, was we say it on

0:27:00.400 --> 0:27:03.240
<v Speaker 2>whether they felt their voice mattered. We asked them whether

0:27:03.240 --> 0:27:06.800
<v Speaker 2>they felt they understanded other people's perspectives better than they

0:27:06.800 --> 0:27:08.879
<v Speaker 2>did before. And then third was where they think they

0:27:08.920 --> 0:27:11.040
<v Speaker 2>believe their input will lead to a better outcoming. On

0:27:11.080 --> 0:27:14.880
<v Speaker 2>all of those, it was like eighty percent plus satisfaction.

0:27:15.040 --> 0:27:19.199
<v Speaker 1>And were you surprised by the outcome? And how do

0:27:19.240 --> 0:27:21.720
<v Speaker 1>you how do you kind of measure the success of

0:27:21.800 --> 0:27:22.600
<v Speaker 1>a project like this.

0:27:23.440 --> 0:27:26.560
<v Speaker 2>I think it was that that people wanted to come

0:27:26.560 --> 0:27:30.679
<v Speaker 2>out and they did. I think that there's two There's like,

0:27:31.880 --> 0:27:33.359
<v Speaker 2>there's two parts of my brain. There's a part of

0:27:33.400 --> 0:27:36.199
<v Speaker 2>brains like did we achieve our you know, like our

0:27:36.200 --> 0:27:39.359
<v Speaker 2>product development goals or are like whatever engagement goals? And

0:27:39.400 --> 0:27:41.080
<v Speaker 2>then there's an other part of me that's like can

0:27:41.119 --> 0:27:45.679
<v Speaker 2>I retain my hope for like society thriving as we

0:27:45.760 --> 0:27:48.119
<v Speaker 2>get more at Amize And so I feel like on

0:27:48.200 --> 0:27:50.560
<v Speaker 2>both of those I felt really good. I felt good

0:27:50.560 --> 0:27:53.479
<v Speaker 2>because obviously the technology wor I also, you know, the

0:27:53.520 --> 0:27:55.439
<v Speaker 2>sense of the team is that we can push to

0:27:55.520 --> 0:27:59.080
<v Speaker 2>do more. And so now we're exploring what other types

0:27:59.119 --> 0:28:02.000
<v Speaker 2>of conversations in the conversation space. How could we Maybe

0:28:02.040 --> 0:28:04.520
<v Speaker 2>it's a different scale, you know, maybe it's a different

0:28:04.560 --> 0:28:08.200
<v Speaker 2>nature of conversation. Maybe we're using different capabilities of Gemini

0:28:08.920 --> 0:28:11.919
<v Speaker 2>to do more to bring people together, to have them

0:28:11.960 --> 0:28:14.880
<v Speaker 2>understand each other more and then having it count for them.

0:28:15.000 --> 0:28:17.359
<v Speaker 2>So that's the kind of fun and exploration phase that

0:28:17.359 --> 0:28:18.000
<v Speaker 2>we're in. Now.

0:28:18.560 --> 0:28:20.280
<v Speaker 1>There's another thing that came out of the research I

0:28:20.359 --> 0:28:21.359
<v Speaker 1>think called grounding.

0:28:21.920 --> 0:28:24.840
<v Speaker 2>Yeah, can you talk a bit about rounding. Groundings their

0:28:25.440 --> 0:28:29.520
<v Speaker 2>sister to hallucinations, So, which is a funny term that

0:28:29.960 --> 0:28:33.040
<v Speaker 2>has widespread adoption to describe when air models may make

0:28:33.080 --> 0:28:33.640
<v Speaker 2>stuff up.

0:28:33.680 --> 0:28:36.480
<v Speaker 1>Because of course that the very bad scenario. Who would

0:28:36.480 --> 0:28:37.919
<v Speaker 1>be that the air was making up policy of pro

0:28:37.960 --> 0:28:38.960
<v Speaker 1>puzzles and no one asked for.

0:28:39.040 --> 0:28:42.000
<v Speaker 2>Oh my goodness, imagine that in such a high stakes

0:28:42.160 --> 0:28:44.360
<v Speaker 2>context where people are trying and talk to their policy maker.

0:28:44.840 --> 0:28:46.880
<v Speaker 2>And there were some funny things that in our early

0:28:46.960 --> 0:28:51.200
<v Speaker 2>testing we were finding it would kind of conflate two

0:28:51.200 --> 0:28:54.320
<v Speaker 2>topics that seem like they're about the same thing, or

0:28:54.360 --> 0:28:57.640
<v Speaker 2>that like you would it would have only one. There'd

0:28:57.640 --> 0:28:59.520
<v Speaker 2>be like one post about you know, there should not

0:28:59.560 --> 0:29:04.800
<v Speaker 2>be some any sweet tuitets offered at in kindergarten. And

0:29:04.840 --> 0:29:08.000
<v Speaker 2>then the summarizing AI would be like the people of

0:29:08.080 --> 0:29:10.520
<v Speaker 2>Bowling Green, you do not want to have like cakes

0:29:10.560 --> 0:29:12.800
<v Speaker 2>and chocolates and candy and they would rather have like

0:29:13.000 --> 0:29:16.000
<v Speaker 2>keep compisation. It's just like wait, whatever that wasn't said, yos,

0:29:16.280 --> 0:29:18.480
<v Speaker 2>that's out of lines. So part of kind of making

0:29:18.520 --> 0:29:21.240
<v Speaker 2>sure that we understood the ways that the AI might

0:29:21.480 --> 0:29:24.479
<v Speaker 2>misfire was doing a ton of testing with humor reviews

0:29:24.960 --> 0:29:27.600
<v Speaker 2>and on previous data sets, and then we did it

0:29:27.760 --> 0:29:30.480
<v Speaker 2>kind of kind of like a small private trial a

0:29:30.520 --> 0:29:33.640
<v Speaker 2>few months earlier with Bowling Green and the local partners

0:29:33.720 --> 0:29:35.960
<v Speaker 2>reviewing them and just seeing like how might it go wrong?

0:29:36.360 --> 0:29:38.040
<v Speaker 2>And then some of the tweets we made with to

0:29:38.080 --> 0:29:41.480
<v Speaker 2>make sure that we batched appropriately. So grounding is every

0:29:41.520 --> 0:29:46.720
<v Speaker 2>output that the AI produces is that it's like appended

0:29:46.760 --> 0:29:51.360
<v Speaker 2>with citations. So everything that the AI says was a

0:29:51.400 --> 0:29:55.320
<v Speaker 2>theme in the report, you can say, you know, show

0:29:55.360 --> 0:29:58.080
<v Speaker 2>me receipts and it will take you to the underlying

0:29:58.360 --> 0:30:01.320
<v Speaker 2>policy proposals or words, the actual words of the people

0:30:01.360 --> 0:30:03.640
<v Speaker 2>at Bowling Green, so you don't have to you know,

0:30:03.720 --> 0:30:05.000
<v Speaker 2>you actually don't have to trust the AI.

0:30:05.280 --> 0:30:09.280
<v Speaker 1>Super interesting. Could grounding be used beyond this context? Do

0:30:09.280 --> 0:30:09.600
<v Speaker 1>you think?

0:30:09.720 --> 0:30:12.400
<v Speaker 2>Yeah? Yeah, and it is it's used broadly. Have you

0:30:12.440 --> 0:30:15.400
<v Speaker 2>ever used notebook M Yes, So they use grounding. So

0:30:15.440 --> 0:30:17.880
<v Speaker 2>when they you know, when they summarize a you can

0:30:17.920 --> 0:30:21.240
<v Speaker 2>give it like one hundred reports or whatever backad Democrat.

0:30:21.280 --> 0:30:23.200
<v Speaker 2>Of course, if you're don't research and it'll tell you

0:30:23.240 --> 0:30:25.320
<v Speaker 2>the themes and then it gives you like like footnotes

0:30:25.680 --> 0:30:28.520
<v Speaker 2>so that you can see where the source material and

0:30:28.560 --> 0:30:29.920
<v Speaker 2>have confidence that at accurate.

0:30:31.000 --> 0:30:34.760
<v Speaker 1>What's the wider hope with a project like Bowling Green?

0:30:34.840 --> 0:30:37.160
<v Speaker 1>I mean, do you is that, like, is there an

0:30:37.160 --> 0:30:39.720
<v Speaker 1>ideal community size? Why this is will be perfect for

0:30:39.800 --> 0:30:42.360
<v Speaker 1>towns that are growing of around one hundred thousand people

0:30:42.400 --> 0:30:44.600
<v Speaker 1>because like you have enough people in the community, you're

0:30:44.600 --> 0:30:47.160
<v Speaker 1>invested in the future because it's growing, it's not so

0:30:47.320 --> 0:30:49.480
<v Speaker 1>large that you get so many proposals that it's like

0:30:49.520 --> 0:30:52.560
<v Speaker 1>impossible to sort. And therefore maybe there's like another one

0:30:52.600 --> 0:30:56.720
<v Speaker 1>hundred Bowling Greens in the US, or is it more like, Wow,

0:30:56.760 --> 0:30:59.920
<v Speaker 1>the greatest problem in US politics today is that participation

0:31:00.080 --> 0:31:03.800
<v Speaker 1>and is low and conversation is toxic, and therefore, like

0:31:03.840 --> 0:31:06.400
<v Speaker 1>this could be a much wider solution, Like how do

0:31:06.480 --> 0:31:09.000
<v Speaker 1>you between those two things? Like where do you feel

0:31:09.480 --> 0:31:09.720
<v Speaker 1>for me?

0:31:09.800 --> 0:31:12.240
<v Speaker 2>The more conversation, the better. I think the reverse is

0:31:12.280 --> 0:31:14.800
<v Speaker 2>also true, the less conversation the worst, the worst we

0:31:14.920 --> 0:31:17.680
<v Speaker 2>ended up. I understand each other, and that was kind

0:31:17.720 --> 0:31:20.880
<v Speaker 2>of where we had taken perspective for over the last

0:31:20.920 --> 0:31:24.000
<v Speaker 2>couple of years, was actually not just helping publisher score

0:31:24.040 --> 0:31:26.800
<v Speaker 2>things that they might want to see less of, like toxicity,

0:31:27.080 --> 0:31:29.680
<v Speaker 2>but actually doing the bridging attributes as well, which was

0:31:29.800 --> 0:31:32.920
<v Speaker 2>the things that kind of help people who disagree stay

0:31:32.960 --> 0:31:35.720
<v Speaker 2>in the conversation together. So I would like to see

0:31:35.840 --> 0:31:38.640
<v Speaker 2>more of it. And I think we've also shown we

0:31:38.720 --> 0:31:40.720
<v Speaker 2>show that the appetite is there from people and that

0:31:40.840 --> 0:31:42.840
<v Speaker 2>it can go really well. That I think the judge

0:31:42.880 --> 0:31:46.360
<v Speaker 2>executive Doug Gorman in our case, was really really brave

0:31:46.840 --> 0:31:49.320
<v Speaker 2>to try and do that. Why brave because the safe

0:31:49.400 --> 0:31:51.800
<v Speaker 2>nobody shows up, or say if they show up and

0:31:52.000 --> 0:31:56.720
<v Speaker 2>you know it's Cook Coffinus. But now seeing what Doug's

0:31:56.760 --> 0:31:59.480
<v Speaker 2>that actually Doug came to So Google has a developer

0:31:59.520 --> 0:32:02.280
<v Speaker 2>conference equal to I Air. That just happened, and Google

0:32:02.280 --> 0:32:05.040
<v Speaker 2>invited Doug to go and participate in the local leaders track,

0:32:05.320 --> 0:32:07.360
<v Speaker 2>and he went and told all these like other mayors

0:32:07.400 --> 0:32:09.440
<v Speaker 2>and local leaders and they were They really lit up.

0:32:09.480 --> 0:32:12.040
<v Speaker 2>And so from what I understand, I think there is

0:32:12.080 --> 0:32:13.880
<v Speaker 2>a lot of appetite to do this, and I think

0:32:13.960 --> 0:32:16.760
<v Speaker 2>him going first and having the bowling green kind of

0:32:17.160 --> 0:32:21.200
<v Speaker 2>proof of concept will inspire others to adopt it to.

0:32:21.760 --> 0:32:23.760
<v Speaker 1>Is that part of your work or in a sense

0:32:23.800 --> 0:32:26.160
<v Speaker 1>you like the fire and it's of other people to

0:32:27.480 --> 0:32:30.080
<v Speaker 1>kindle it bad metaphor, but I mean otherwise do what

0:32:30.120 --> 0:32:31.520
<v Speaker 1>are you going to do the same experiment again in

0:32:31.520 --> 0:32:34.040
<v Speaker 1>another community or a larger community? Or do you feel

0:32:34.040 --> 0:32:36.760
<v Speaker 1>like you have the proof point that you think you know?

0:32:36.800 --> 0:32:38.560
<v Speaker 2>I think it's you know this question, like what is

0:32:38.600 --> 0:32:41.640
<v Speaker 2>Jigsaw's highest and best use. I think if we if

0:32:41.640 --> 0:32:44.120
<v Speaker 2>now we've done the concept, if there's another proof like

0:32:44.200 --> 0:32:46.600
<v Speaker 2>another place that we'd want to do it, I think

0:32:46.800 --> 0:32:48.480
<v Speaker 2>we would be up into that if it felt that

0:32:48.560 --> 0:32:53.200
<v Speaker 2>it was some additive like materially additive, if it were

0:32:53.560 --> 0:32:55.840
<v Speaker 2>you know, maybe at the state level or you know,

0:32:55.880 --> 0:32:58.200
<v Speaker 2>some other kind of complex conversation that we felt that

0:32:58.200 --> 0:33:02.640
<v Speaker 2>we could we could advance. I think there will be others,

0:33:02.920 --> 0:33:07.440
<v Speaker 2>like kind of implementers who will I think this will

0:33:07.480 --> 0:33:11.520
<v Speaker 2>become a kind of a commercial opportunity for others to

0:33:11.520 --> 0:33:13.120
<v Speaker 2>help with. I don't think we do you know, that's

0:33:13.120 --> 0:33:15.240
<v Speaker 2>the best use of Jigsaw to do many many more

0:33:15.240 --> 0:33:16.719
<v Speaker 2>of the same thing in towns.

0:33:17.160 --> 0:33:20.000
<v Speaker 1>So in other words, like a polling firm could take

0:33:20.000 --> 0:33:22.800
<v Speaker 1>it over as a business. Liane Audrey Tang, who I

0:33:22.840 --> 0:33:25.120
<v Speaker 1>know that you were, you know, to spend time with.

0:33:25.960 --> 0:33:29.120
<v Speaker 1>Recently an interview with Nick Thompson and Audrey spoke about

0:33:30.160 --> 0:33:33.560
<v Speaker 1>the Bowling Green project and in the context of how

0:33:34.160 --> 0:33:38.120
<v Speaker 1>in California post fires, Newsome is sort of using this

0:33:38.240 --> 0:33:44.960
<v Speaker 1>tech enabled participatory democratic technology to understand what people want

0:33:44.960 --> 0:33:49.120
<v Speaker 1>from the rebuild of like burnt areas of LA. Where

0:33:49.120 --> 0:33:52.240
<v Speaker 1>does this fit in with the kind of larger tech

0:33:52.360 --> 0:33:56.120
<v Speaker 1>enabled digital democracy trend.

0:33:56.600 --> 0:33:59.040
<v Speaker 2>I think it is. I think they're pioneering there because

0:33:59.080 --> 0:34:01.720
<v Speaker 2>they're doing at the state level in California's a massive state.

0:34:02.800 --> 0:34:05.080
<v Speaker 2>And interestingly, when we did it in Bowling Green, which

0:34:05.160 --> 0:34:08.000
<v Speaker 2>was you know, Well or Warren County, much much smaller,

0:34:08.440 --> 0:34:11.680
<v Speaker 2>super open question. In the case of California, that asking

0:34:11.680 --> 0:34:14.480
<v Speaker 2>a really narrow question. I mean, it's big in itself,

0:34:14.480 --> 0:34:16.560
<v Speaker 2>but it's much more focused question, which is, you know,

0:34:16.800 --> 0:34:19.000
<v Speaker 2>how are we going to where do we go from

0:34:19.040 --> 0:34:23.120
<v Speaker 2>here after the wildfire specifically, So I think that's really instructive,

0:34:23.160 --> 0:34:25.160
<v Speaker 2>Like doesn't once you go to the state level, doesn't

0:34:25.200 --> 0:34:27.839
<v Speaker 2>need to be so focused And they've already got quite

0:34:27.840 --> 0:34:30.120
<v Speaker 2>a bit of engagement and is she going to talk

0:34:30.120 --> 0:34:31.719
<v Speaker 2>to them about how they're going to make sense of

0:34:31.760 --> 0:34:33.920
<v Speaker 2>it because I don't know what they do. Well, yeah,

0:34:33.440 --> 0:34:36.360
<v Speaker 2>we're in touch with them. And Audrey, who's you know,

0:34:36.360 --> 0:34:38.759
<v Speaker 2>it was the former the kind of in agural like

0:34:38.800 --> 0:34:41.839
<v Speaker 2>I guess did a digital minister for Taiwan is like

0:34:41.880 --> 0:34:44.520
<v Speaker 2>this just she's just a global matchmaker. So she's advising

0:34:44.880 --> 0:34:47.960
<v Speaker 2>California as well, and she's advised us a lot. She's

0:34:48.280 --> 0:34:49.920
<v Speaker 2>been very instructive for our work.

0:34:50.480 --> 0:34:53.640
<v Speaker 1>Two questions are close, I guess the first. Obviously, it's

0:34:53.680 --> 0:34:57.880
<v Speaker 1>a moment where Google is being you know, the subject

0:34:57.920 --> 0:35:01.560
<v Speaker 1>of the Justice Department and Chrome. All this kind of stuff,

0:35:01.840 --> 0:35:05.120
<v Speaker 1>in other words, is a target of federal investigation when

0:35:05.120 --> 0:35:08.319
<v Speaker 1>you work with government, like do you are there people

0:35:08.320 --> 0:35:10.600
<v Speaker 1>who say this is a conflict of interest or Jigsaw

0:35:10.680 --> 0:35:14.799
<v Speaker 1>being in bed with local estate governments is somehow advancing

0:35:14.840 --> 0:35:17.600
<v Speaker 1>Google's corporate motives elsewhere, and that's a problem.

0:35:18.360 --> 0:35:22.920
<v Speaker 2>In this case. We didn't actually have any commercial relationship

0:35:23.000 --> 0:35:26.160
<v Speaker 2>with the government. We were actually partnered with the group

0:35:26.200 --> 0:35:28.560
<v Speaker 2>the I told you about Innovation Engine and they did

0:35:28.600 --> 0:35:31.040
<v Speaker 2>the kind of execution. But we did care a lot

0:35:31.080 --> 0:35:35.759
<v Speaker 2>about making the tech work for the hotesty maker and

0:35:35.800 --> 0:35:38.200
<v Speaker 2>the people because it seemed it seems like such an

0:35:38.239 --> 0:35:40.400
<v Speaker 2>important thing for us to get right. And if we

0:35:40.440 --> 0:35:43.919
<v Speaker 2>want policy makers to make good policy, even about tech,

0:35:43.960 --> 0:35:45.560
<v Speaker 2>they should kind of understand it and see how it

0:35:45.560 --> 0:35:47.640
<v Speaker 2>shows up for them. And in this case, I think

0:35:47.680 --> 0:35:49.640
<v Speaker 2>Voluningroom was so attractible was so because it was so

0:35:49.800 --> 0:35:54.440
<v Speaker 2>mixed in terms of politics. So, yeah, we've always kind

0:35:54.480 --> 0:35:57.640
<v Speaker 2>of been in and we've always been around governments, and

0:35:57.680 --> 0:36:02.000
<v Speaker 2>actually still this point you haven't actually directly partnered with

0:36:02.120 --> 0:36:06.640
<v Speaker 2>the government. But it felt really gratifying to feel like

0:36:06.719 --> 0:36:09.320
<v Speaker 2>you are helping that dynamic work better.

0:36:09.760 --> 0:36:12.040
<v Speaker 1>What do you still have to achieve a Jigsaw and

0:36:12.360 --> 0:36:15.279
<v Speaker 1>what kind of future signature projects can you tease for

0:36:15.360 --> 0:36:16.239
<v Speaker 1>us Today.

0:36:17.160 --> 0:36:21.960
<v Speaker 2>We're noticing a change in the culture at Jigsaw internally

0:36:22.360 --> 0:36:26.720
<v Speaker 2>that actually mirrors what's happening externally. That things are moving

0:36:26.800 --> 0:36:30.640
<v Speaker 2>so fast, like the technology is moving so fast, and

0:36:30.719 --> 0:36:33.680
<v Speaker 2>we are inside this incredible tech company, and so I

0:36:33.680 --> 0:36:36.279
<v Speaker 2>think we sometimes maybe things appear to us before they

0:36:36.360 --> 0:36:38.399
<v Speaker 2>mate to others. I think we could help a lot

0:36:38.440 --> 0:36:40.959
<v Speaker 2>by kind of sharing as we're going. So I think

0:36:41.320 --> 0:36:43.880
<v Speaker 2>the change in the coming year that I'd like to

0:36:43.880 --> 0:36:48.320
<v Speaker 2>see is more rapid iteration and more, sharing more frequently,

0:36:48.360 --> 0:36:50.399
<v Speaker 2>and being more public about what we're doing.

0:36:50.920 --> 0:36:54.560
<v Speaker 1>Well, you know where to come. Thank you, Thank you

0:36:54.760 --> 0:37:16.719
<v Speaker 1>us for tech Stuff. I'm oz Vaaloshian. This episode was

0:37:16.719 --> 0:37:21.239
<v Speaker 1>produced by Eliza Dennis and Adriana Topia. It was executive

0:37:21.239 --> 0:37:24.680
<v Speaker 1>produced by me Karaen Price and Kate Osborne for Kaleidoscope

0:37:25.120 --> 0:37:29.400
<v Speaker 1>and Katrina Norvel for iHeart Podcasts. Jack Insley mixed this episode,

0:37:29.440 --> 0:37:32.880
<v Speaker 1>and Kyle Murdoch Rodelphime Song join us on Friday for

0:37:32.920 --> 0:37:34.960
<v Speaker 1>the Week in Tech, when we'll run through the tech

0:37:35.000 --> 0:37:39.200
<v Speaker 1>headlines you may have missed. Please rate, review, and reach

0:37:39.239 --> 0:37:53.160
<v Speaker 1>out to us at tech Stuff podcast at gmail dot com.