Pushkin. I'm Maybe Higgins, and this is solvable Interviews with the world's most innovative thinkers who are working to solve the world's biggest problems. My solvable is that every frontline social organization is the ability to use data and AI same way, same capacity that the big tech companies do today. I want to see a world where the same algorithms that are routing your packages to you your house coming so efficiently because an AI figured out the best way to avoid traffic and weather are just as equally being applied to delivering a vaccine through an area before it spoils. That's Jake poor Away, the founder and CEO of nonprofit Data Kind. He's talking to Jacob Weisberg about how he's working to make that world a reality. The Rockefeller Foundation has thought about this too. More than two point five quintillion bytes of data are produced every day. That's one hundred trillion bytes. This abundance of data, combined with rapidly advancing analytics capabilities, could really improve the lives of billions of people around the world, but it's only living up to a fraction of that potential. While private sector businesses have been building and deploying data science capabilities for many years now. Most organizations in the nonprofit and civic and public sectors are way behind. Of course, they want to use the applied data to make their work go farther and faster and to help more people, but they don't often have the resources. I mean, put yourself in the shoes of a newly minted graduate. They're probably wearing tivas. Last year, the San Francisco Chronicle analyze glass door data of the starting salaries of some of the biggest tech companies in the Bay Area. They found out that tech pays even for the young and inexperienced. The average starting salary for a software engineer was almost ninety two thousand dollars. So there's the workers and then there's the technology itself. We know the power data science can have for social good because we've seen it in action. When mission driven organizations have the right talent and tools and knowledge, data science can generate real human impact, helping vulnerable families access public benefits, saving water and money during droughts, and saving time in resettling refugees so that they can find homes and jobs faster. Jake Borway works on this stuff every day. He's a machine learning and technology enthusiast who loves nothing more than seeing good values in data. In twenty eleven, he found a data Kind, bringing together leading data scientists with high impact social organizations to better collect, analyze, and visualized data in the service of humanity. Jake works to ensure organizations like the Red Cross have access to AI and data science that's as good as the access enjoyed by huge companies like Facebook. Data Kind has twenty thousand volunteers around the world, who he likens to mets on San Frontier, the doctors without Borders, except their data scientists working pro bono with leading social change organizations on all kinds of projects, including one that has data scientists from Netflix predicting water usage in a California neighborhood. It's fascinating, So enjoy this conversation and I'll talk to you after. What's the problem. In a nutshell, the problem is that digital technology and artificial intelligence have exploded over the last ten or fifteen years, which have created huge opportunities in the corporate space or in building new apps for society, but there's very little application of that to social sector causes. So we have this huge opportunity to use a revolutionary technology to predict the future of things, to understand our society better, to automate things that we either don't want to or couldn't do, And yet there's a huge potential loss in that it's very difficult to get that applied to pro social causes that we need. Jake is a data scientist. When did you start to see some of the downsides around big data? Really? The article that I used to point to is like the beginnings of the tide turning to the negative. Was the article that was titled very salaciously, Target Knows You're pregnant, And if you remember this one from twenty thirteen, but the basic idea was that someone had their daughter, that maybe sixteen seventeen year old daughter was receiving mailers from Target that said, Hey, we think you need to buy kupons for baby diapers or formula, and the dad called up, you know Target, all, Matt, So what are you sending me all my daughter all these deals for having babies. She's not pregnant, Like, why are you trying to get her to become pregnant? And the person on the other end of the line, of course didn't know what was happening, because you know, the algorithms just send you what they think you're going to buy based on other stuff you've bought, and it's He called back later, kind of shame facedly and said, you know, I talked to my daughter and actually she is pregnant, and you know, the data had picked up on that simply because you know, it watched what she bought and she was probably buying you know, prenatal care, vitamins and stuff. But that article got shared around as the sign that big data was going to be negative. Target knows you're pregnant. What a horrible invasion of privacy. That title alone should, you know, make everyone's skin crawl. But that's the problem is that that shouldn't be the case. We think of there are so many opportunities to be using data and algorithms to see where disease outbreaks are going to occur or predict in the same way as what kind of conditions you might have so you can live a healthier life. And so I think it was then that we really thought, Okay, we need to come out and show the positive sides of this. Otherwise everyone's going to just run to the fear around what data science can do. We're interested on this podcast and people who've taken this leap to become problem solvers and to take on the biggest problems in the world. What made you take a leap to leave the private sector to start an organization with an ambitious goal. Well, I have to say it was a bit of an accident. Actually it was maybe twenty ten or eleven, and I had just coincidentally come out of school with a computer science and a statistics degree, which little did I know was going to become what would lead to the title data scientist. And I was working at the New York Times R and D Lab, and really what seemed obvious was the fact that we had all of this new digital technology, from cell phones that people were carrying around with them, to satellites launching in the air, to sends being put around the world, that we were digitizing our very existence. We were becoming a digital species. There was almost like a central nervous system to the world, and that meant that were these huge opportunities to learn from that to you know, have algorithms drive maybe our greatest human values. But the folks who really knew how to convert data into those actions. The data scientists were largely locked up in tech companies, and you know, I would actually go to hackathons, which are you know, like weekend events where technologists would get together and just work on whatever they thought was cool. And I would sit there and think, this is so interesting because you know, we're not at a company, we're not at our jobs. We're here on the weekend. You know, I'm sitting next to some machine learning engineer from Google and NASA scientist, and I'm like, this is great. We can make whatever we want. Like the world has just become so ripe for what's possible. And at the end of the day, the stuff that people made was just so unfulfilling. You know that someone had made like Twitter for pets, or had improved how you'd find local deals in your neighborhood, and so I just said, man, there's got to be something more we can do for society, or something more fulfilling really than this, as opposed to solving the problems of very well paid twenty somethings in the Bay Area, right, which is the parody, but that is a lot of the new companies you hear about are solving problems like how do you get your food delivered or god knows how to get cannabis delivered? You know when you when you could already buy it by walking around the corner. You're exactly right. We solve the problems that we ourselves have. And as you've pointed out, the tech community for better for worse, excused young male US. So, yeah, I just thought, you know, what would it take for to be applied to the social sector. Where are the people who are on the front lines of getting people food or clean water? And how could you apply it there? And so I just wanted that job myself. What didn't exist? So I just wrote to a couple of folks in the community here in New York and said, hey, you know, instead of going and building you know, a door dash competitor, could we, I don't know, work with the Red Cross US or Kiva who goes cash transfers to folks, and say what could we do with their data? What could we learn? What are the positive ways we could work together with them? And I thought people would just say, yeah, good idea, Jake, but no thanks. I kind of just buried the little sign up link for folks, and I was surprised to find that people started sharing around before I knew it. I came back to work the next time, hundreds of emails in my inbox from people not just in the city but around the world saying, though this is great, I want to get involved with data kind, I want to do data kind France. At one point, a few months into this, the White House called and said, hey, we're interested in big data initiatives. What's this thing? And you know, joke because I don't know, it's not really a thing, But to me it really tapped into an energy from both the technology side and the nonprofits and governments who are writing, who said, we're energetic to take on this new wave of this technology and figure out how could be applied. And so our job ever since has really just been trying to support that community, harness its energy, and be helpful in any way we can. Since you've been doing this, it's amazing how quickly attitudes have shifted around big data and algorithms. I mean, just think about Facebook, which even a few years ago was thought as a socially positive company. That was why part of why people went to work there, and in just a couple of years it's become something that people think is an overwhelmingly negative force. Are we're swinging too far in the other direction in our skepticism about what data is going to be used for? Well, I think there's a healthy reckoning on how we've been using data and technology in the past. You're right that in the last couple of years there was sort of unfettered techno optimism amongst a lot of the big companies and that this would just change everything and nothing could ever go wrong with social media and data. So I think there is an obviously very healthy reckoning of this, and we're starting to realize what the downsides could be. What your point I think is missing and we really need to get acclimated to, is where do we go from there? You know, is the idea that we're just going to put the genie back in the bottle, not use digital information in these ways, regulate all companies into existence. I'm in favor of, by the way, stronger regulation, for sure, But I think what we need now is more examples and more of a community of practice around what it looks like to use these technologies ethically. That's a big conversation obviously, that's in the space right now. You hear a lot about the ethics of data use, ethics of AI, but even then I find those conversations fairly academic. I think what we need are some more positive examples of how it can be applied and positive principles that we all agree to adhere to. And so the data kind that's something we're really working to try to demonstrate, is to say, yes, we need to protect ourselves, uphold our civil liberties through data. Make sure that we're not degrading human life with what's going on with data in the business world? And what does it look like when you want to use data and algorithms to predict, say, inclement weather that could wipe out a crop and that's critical to someone's sustenance in another part of the world. What's the good version of this? You know? How do you make sure that it's accountable to those folks? How do we make sure that everyone involved has some sense of what the algorithm is doing and how their data is being used. And I don't think we can move past that point just by talking about it. I think we need real concrete examples of data scientists, nonprofits, social organizations, constituents getting together to say, what does the good version of this look like a better version. I should say there was a positive example in the news recently with the prediction of the cyclone in South Asia that killed very few people, and in the world before big data, that same storm might have killed a lot of people through panic, through all sorts of consequences because people wouldn't have known it was coming. I mean, is that the kind of example we're talking about here? Something positive? I think that's exactly right. So at data Kind we team technologists like data scientists who want to volunteer their time alongside social change organizations, be they government agencies or nonprofits who have a pro social mission, might be able to use data and algorithms to do even more, and we together they collaborate and kind of codesign the solutions that they might foster a better world. So some examples that we've seen are exactly what you're talking about. There was a project that a group did as a water district in California, and the problem they faced was when drought season comes, you know, it's really hard to get water to folks. People don't have water. That's obviously problematic. You need drinking water and water to bathe, etc. But more than that, the cost of not getting them water is really high because the only way that they can get water to the places they don't have it is to actually take a dump truck, drive it up to some other reservoir, maybe over to Nevada, literally fill it by hand and drive it back. So you're also facing like huge environmental costs, huge energy costs. So they ask the question, you know, could we figure out a way to predict how much water demand there's going to be at a more granular level so we can really understand and ration more effectively. And so we team them up with some data scientists that come from everywhere from Netflix to environmental science organizations, and together they collected the data at almost a block by block level, and they built an algorithm that sort of takes that data in and continually gives updates. Does water district to say, hey, this is how much we think people are going to use. Here's how much they've already used. Tomorrow, you're probably going to see this, And they said, in the first year of using this, they saved over twenty five million dollars in addition to getting water to people much more effectively. So I think when you hear about cases like that those are the kinds of examples that we want to kind of platform and see more even the world where within the confines of social organization these data and algorithms that can really drive real effectiveness. Now your people are all doing this for good. We've all heard about the kinds of bias issues that have started to turn up with predictive algorithms of different kinds, and they seem to get embedded just because of the inherited unconscious biases of the people who write the algorithm. Absolutely, how do you avoid recapitulating that problem again with the projects you're working on? Such an awesome question, and I think just to comment on the challenge generally, I think you really nailed it there. That the challenge that we face is that humans have been collecting data from our activities that incorporate unconscious bias, and so if you then have a machine learn from it or you analyze it, you write replicating that. So, while I will not admit that we have a perfect solution, because I mean we're sort of talking about the challenge of bias and humanity, some of the things that we really focus on is the technology to us that we're building is secondary to the outcome for people. So, for example, it's not exciting to us to build an algorithm that helps a homeless shelter triage people to the right homeless shelters correctly just because it's a cool algorithm. We only care if at the end of the day, the ultimate success metric that you know, a wide range of inclusive folks are getting housing is achieved. So I want to say that first because I think one of the reasons we see some of these biased challenges rise up is that folks say, hey, the algorithm is doing something. It's doing a thing I want, like giving out sentences in courts or you know, policing folks, but without a question of and how is it biased? Towards the end, you know, what's it achieving. But the other thing we do is we work extremely closely with our NGEO partners who are on the ground and who understand a lot of those challenges. And so we'll actually do what we call a pre mortem some other companies do, which is before we even start a project, we'll say, okay, let's pretend we jump to the end. Well, you know, basic questions like how will this be maintained, who's actually going to use this tool at the end of the day. But then we'll also ask two questions, which is one, what's the worst that happens if we fail? So if you're relying on us to build, this is not something we would necessarily build. But let's say someone said, hey, we want a tool that predicts whether you have cancer or not. Okay, well that's pretty serious. And if we don't succeed, are you stuck because you really needed that and now your organization can't proceed. That's important to know. But then we also ask what's the worst that happens if we succeed? So who is this going to affect? How would you know that it's wrong? Right? Like, how would you know just because it's chugging away making predictions? Is it doing the right thing? Is it disenfranchising certain groups? Could somebody use it to intentionally target people who have cancer? We ask a lot of those questions, and what's really important us in that questioning is who has the power and agency to both understand the algorithm and change the algorithm Because in the current landscape, when tech companies build algorithms, it's not much you can do. But you know, I don't have enough agency to know how Facebook's news feed algorithm works, nor can I really affect it much? But that's not acceptable to me when you're bringing algorithms into the public good space and this is actually affecting folks lives. So those are some of the questions we ask up front and really try to be rigorous with our partners around oversight of and oftentimes that's enough for us to not take on a project. It's great that you're thinking steps ahead about these projects, and your own solvable is, ironically, to put yourself out of business is to create a world in which you don't need a data kind to point people towards positive uses of data. That's right, What would it take to make that happen? And I guess playing your chess game. What happens when that happens. The day we close our doors is the data. Every frontline social change organization has the capabilities to use data and AI the same way the big tech companies do ethically and capably. And so you know, our little slice of that today is to bridge the gap in getting the human capital, the talent, the data scientists AI engineers to social organizations. That sort of step one is to show people the art of the possible and really get some of those challenges solved. But what do it take to do that? Long runs to think about what are the problems and hurdles we're trying to overcome with that model today, and they are that in the social sector there isn't enough awareness about what the technology could do or where it would be applied. So we have to start with that, and I think now increasingly you're seeing more of more folks understanding that, more companies talking about doing data and AI for good. So I feel like there's some progress there, But if you go further, you have to think, well, how would a government or nonprofit get access to these resources in the long term, And there I think there's going to be a long term shift in getting funding to move towards nonprofits for things like data science and AI. You're going to need maybe consultancies that actually provide this service in the social sector. There's lots of different models for where that capacity could come from, but I think the biggest things that we need right now are that awareness of how could be used and then the I say, the funding for ngox to be able to hire a data sciences and incorporate them into the work they do. Now. When that happens, what happens. Oh, I mean, I'd love to say that all challenges that are stymied by not having data science and AI are solved live apply ever after. But actually, what I think my most ambitious hope for the world is that we could actually tip the balance a little bit to where the social sector is paving the path for how machine learning and AI could be used. I think we're so built into this default model that business and wealthy countries set the agenda and everyone else kind of struggles to catch up and imitate. We're talking about a technology that is so fundamental to humanity because it relies on data about us. When we talk about AI, it is like automating human processes that I don't think that's something that should be just a business application that is ported to the world. There should be a place for us to say, what does it look like when we apply the technology to the better angels of our nature? What is human based AI? What are the things we care about? And I can't think of any other place besides the social sector whose sole mandate is to look out for humanity. So my dream is when you bridge that gap, when that's there. You could actually have this voice from the social sector itself saying what it looks like to have human based ai Jick. Do you think about the training of data scientists. I sometimes think we're just missing the intersection between moral philosophy and computer science. You know, the people who are majoring in college and electronic engineering aren't reading much Kant, and the people who are reading Kant don't understand much about computer programming, you know, And in a way, the problem is that the people at these tech companies don't have a different kind of background in literature and philosophy and history to think through the implications of what they're building the way you clearly are thinking through those implications. I think it's a really great point that when wielding the technology, it's really important to have a very varied sense of skills somewhere in the conversation. And increasingly you're seeing data science and tech curricula incorporate ethics training into their courses, which I think is great. In the same way that I'm not a historian myself, I feel like physics went through this reckoning with the ethics of what was being built when they went from the joy of all energy and nuclear power to the realizations of the downsides of the nuclear bomb nuclear weapons. So I think you're going to see that similar shift, which is which is great, But you know, I think what your question raises actually a bigger point to me, which is who holds the responsibility for the ethical applications of this technology? And I'll just say, while I would love to see, you know, ethical code around data science, it's a lot of responsibility to say that engineer x it has come out of college engineering college for two years and is working at big tech company and gets asked by their boss to build something fairly benign, like I upgrade to their their GPS system that recommends routes you can walk that avoid crime ridden areas. I say, here's an algorith build that. Well, number one, that's not necessarily a bad thing to builds not like you know, it's not as black and white as some people may feel about building a weapon or something. But of course, if you sort of play the game through, if everyone were using an app that avoided crime ridden areas, probably end up with some sort of digital segregation. So number one, there's already long range effects that you'd have to anticipate. But more than that, It also relies on that, you know, second year engineer to say, hey boss, yeah, I'm not doing that. You know this is I'm quitting, which, given you know people's career paths and the money associate with these jobs, is a big ask. So I would say it's not just about the technologies. I think the question is, you know, how do we share that responsibility? Is it the technologist to make this call? Was it the manager said we want to build this feature? Was it the constituents would be affected by that? Is a government to come regulate. I don't think there's any one answer, but I do think the frame that people have I'm hearing more in the public right now around technologists need to know the ethics, I think is missing the bigger picture that that alone isn't the right responsibility model. In my mind. You have two very different ideas of capitalism, right. I mean, there's an older idea that government sets the rules, tells you what you can and can't do, and that businesses should obey the law and regulation but go be very free to do what they want. Within that, the newer model suggests that the businesses themselves have a higher degree of social responsibility, and it's not enough to follow the rules that they have to be thinking about outcomes. Look, I would love to live in a world where business and social outcome were somehow linked, where the fact that businesses were accountable somehow to at least not doing harm, if not improving human life. That would be a really great intersection. Call me a cynic, but we're not really currently set up for that. The incentives aren't there. In my mind, businesses are still held mostly to the bottom line, even though we are seeing some increased interest in social entrepreneurship, where businesses may have a double bottom line, one that's monetary and one that's social, or new structures like b corps that actually say, hey, we are committed to some social cause. But I think it's a lot to ask of a company. And as much as it's a nice idea of a future of capitalism, it's certainly not the rule or the law. And so I don't think that's going to be the sole model that brings us to a world of pro social technology and AI. If for no other reason then certain human needs are inherently cost ineffective, I would say to solve at least currently if people could cry those if every social problem were able to align perfectly with a business needs, be in great shape. But when it comes to housing the homeless or making sure that people have food to eat, that is a difficult challenge that I don't see an immediate market solution too, and so I don't think even the best intention companies could survive in a market based world trying to solve that problem. I mean, Google, which is still the first and best known data company essentially has held out this promise that we're going to make a lot of money using data commercially targeting advertising, but we're going to use a lot of what we make, or at least some of it in a kind of philanthropy. We're going to try to create some of the kinds of solutions you're talking about that aren't driven by the profit motive. Does that work look like? I said, One of the big challenges we face, I think in the social sector right now is the lack of funding for innovation for your technology. And so if company are going to offer that great netwin, do I believe that the world's biggest challenges will be solved on the you know, philanthropic efforts of large companies that I'm not so hopeful. I think there. I still wonder where are the folks for whom the mandate is solely pro social, you know, for governments or again nonprofits or civic organizations whose very guiding mission is to make sure that human prosperity is enhanced. There's a little bit more of a direct line there. And so that's why I think it has to be a combination of the two, and why we focus so much on saying instead of trying to bend the Googles of the world to you know, being in charge of clean water, which frankly I think is really not not the way you want to go. Where the you know, the clean water organizations of the world who just need that same technology to be ten hundred times more effective. What are some things listeners to this podcast might be able to do to work towards the kinds of solutions you're thinking about. Well, the great thing about this cross cutting technology is that everyone has a role to play in creating this future vision of more social and positive AI. Well, first, I would say, if you're a technologist who works with data and you want to give your time and energy back, come aboard. There's a whole movement of folks doing this work. Whether you want to come work with us at Data Kind and work on projects pro bono, or with many of the other organizations like Driven Data, Data Science for Social Good, CODE for America who take technologists and apply them to social problems, come aboard. There's no reason to wait. And increasing Link asked the company you work for if there's opportunities to give back, because we see more tech companies do that. But if you're not a data scientist, non data scientist, I would say, yeah, I have to first give a shout out to anyone of the funder or donor world. One of the big gaps here is that there isn't enough funding for technology and innovation in the social sector. So I've been very impressed with the efforts of Rockefeller Foundation and MasterCard Impact Fund and others who are giving big amounts of funding to data and AI and social good to bring it on. We need more of that for this happen. But very lastly, if not a data scientist and you're not a funder, I would say there's a huge opportunity to get involved in just understanding what this new technology can do. Ciicero had a quote that you should take an interest in politics, because politics is definitely going to take an interest in you. And I feel exactly the same about data and algorithms. They're going to take an interest in all of us. In fact, they're shaping our lives already today. Maybe the reason you're listening to this podcast is because an algorithm recommended it to you based on your previous listening habits. And so if these tools are going to be shaping and visibly shaping our decisions, then it's all the more incumbent on us as society to understand what the ramifications are, where it's showing up in society, and how we might have some agency over the role we want it to play. I think so much of the reason you hear so much negativity today is because we don't understand it well enough and we don't have any agency to change it. So our only options are to shrug and say, well, I guess that's going to be the way it is, or to rail against it and say this is bad. But if we could get to a place where we had call it algorithmic literacy. Not everyone needs to code, but if you just understand a little more about it, then I think we'd progress towards a society where we felt like we had a more control agency over how we work with the machines instead of against them. That's a great point. And I have to ask you for a reading recommendation. If people need to get educated, what should they read. What's a thing or two they should read to get more sophisticated about data. So the best thing I think you can read are some of the blogs that actually talk about the state of the space today, because it's changing so much that you know there's no one book that's going to capture it. Yeah. So some of the ones I love are the company O'Reilly O'Reilly dot com. They have a feature on data and AI that's a weekly newsletter that comes out talking about everything from the interesting innovations and AI to what kind of privacy concerns are in the space today, and it's very readable for a common audience. I think that's one of the most interesting ones. I would also read Data and Society's newsletter. They are a group here in New York who are really tackling the question of what does it mean to have data and algorithms in society. They have some really great accessible writing there The other thing I would say is if you have the privilege of living near a medium, miss or big city that has a meetup community. There are tons of data science AI meetups where people go and just talk about what's going on in the space. And I always recommend that people drop by at least one because if you see it and feel it and here people are talking about you don't have to understand, you know, if there's any math on the board, but just you almost immediately, it creates a states where people walk and go, oh, I actually see what this is all about. So I would say if you happen to be a checkout meetup, dot com or any of those communities. The data scientists AI folks are very friendly and I know you'll have a great time, if not an educational one. Terrific. Well, Jake Probi, thanks for joining us Unsolvable My pleasure. Thanks so much for having me reasons for hope all of this potential being harnessed to improve people's lives, the really big stuff. Although my ears certainly did prick up when Jake mentioned Twitter for pets, as did my dog's ears. She has been dying to get online and really drag other dogs anonymously, of course, but both myself and my dog are pleased to see what data Kind has actually managed to do so far, creating algorithms that have helped transport clean water more effectively, informed government policy that protects communities from corruption, and detected crop disease using satellite imagery. Jake and his team and all those volunteers are leveling the playing fields and you can help too. Read more about data Kind and how to get involved at Rockefella Foundation dot org. Slash solvable. Solvable is a collaboration between Pushkin Industries and the Rockefella Foundation, with production by Chalk and Blade. Pushkin's executive producer is Mia LaBelle. Engineering by Jason Gambrell and the fine folks at GSI Studios. Original music composed by Pascal Wise. Special thanks to Maggie Taylor, Heather Faine, Julia Barton, Carlie Migliori, Sheriff Vincent, Jacob Weisberg, and Malcolm Gladwell. You can learn more about solving today's biggest problems at Rockefella Foundation dot org. Slash Solvable. I'm Mave Higgins, Now go solve Itt

Pushkin. I'm Maybe Higgins, and this is solvable Interviews with the world's most innovative thinkers who are working to solve the world's biggest problems. My solvable is that every frontline social organization is the ability to use data and AI same way, same capacity that the big tech companies do today. I want to see a world where the same algorithms that are routing your packages to you your house coming so efficiently because an AI figured out the best way to avoid traffic and weather are just as equally being applied to delivering a vaccine through an area before it spoils. That's Jake poor Away, the founder and CEO of nonprofit Data Kind. He's talking to Jacob Weisberg about how he's working to make that world a reality. The Rockefeller Foundation has thought about this too. More than two point five quintillion bytes of data are produced every day. That's one hundred trillion bytes. This abundance of data, combined with rapidly advancing analytics capabilities, could really improve the lives of billions of people around the world, but it's only living up to a fraction of that potential. While private sector businesses have been building and deploying data science capabilities for many years now. Most organizations in the nonprofit and civic and public sectors are way behind. Of course, they want to use the applied data to make their work go farther and faster and to help more people, but they don't often have the resources. I mean, put yourself in the shoes of a newly minted graduate. They're probably wearing tivas. Last year, the San Francisco Chronicle analyze glass door data of the starting salaries of some of the biggest tech companies in the Bay Area. They found out that tech pays even for the young and inexperienced. The average starting salary for a software engineer was almost ninety two thousand dollars. So there's the workers and then there's the technology itself. We know the power data science can have for social good because we've seen it in action. When mission driven organizations have the right talent and tools and knowledge, data science can generate real human impact, helping vulnerable families access public benefits, saving water and money during droughts, and saving time in resettling refugees so that they can find homes and jobs faster. Jake Borway works on this stuff every day. He's a machine learning and technology enthusiast who loves nothing more than seeing good values in data. In twenty eleven, he found a data Kind, bringing together leading data scientists with high impact social organizations to better collect, analyze, and visualized data in the service of humanity. Jake works to ensure organizations like the Red Cross have access to AI and data science that's as good as the access enjoyed by huge companies like Facebook. Data Kind has twenty thousand volunteers around the world, who he likens to mets on San Frontier, the doctors without Borders, except their data scientists working pro bono with leading social change organizations on all kinds of projects, including one that has data scientists from Netflix predicting water usage in a California neighborhood. It's fascinating, So enjoy this conversation and I'll talk to you after. What's the problem. In a nutshell, the problem is that digital technology and artificial intelligence have exploded over the last ten or fifteen years, which have created huge opportunities in the corporate space or in building new apps for society, but there's very little application of that to social sector causes. So we have this huge opportunity to use a revolutionary technology to predict the future of things, to understand our society better, to automate things that we either don't want to or couldn't do, And yet there's a huge potential loss in that it's very difficult to get that applied to pro social causes that we need. Jake is a data scientist. When did you start to see some of the downsides around big data? Really? The article that I used to point to is like the beginnings of the tide turning to the negative. Was the article that was titled very salaciously, Target Knows You're pregnant, And if you remember this one from twenty thirteen, but the basic idea was that someone had their daughter, that maybe sixteen seventeen year old daughter was receiving mailers from Target that said, Hey, we think you need to buy kupons for baby diapers or formula, and the dad called up, you know Target, all, Matt, So what are you sending me all my daughter all these deals for having babies. She's not pregnant, Like, why are you trying to get her to become pregnant? And the person on the other end of the line, of course didn't know what was happening, because you know, the algorithms just send you what they think you're going to buy based on other stuff you've bought, and it's He called back later, kind of shame facedly and said, you know, I talked to my daughter and actually she is pregnant, and you know, the data had picked up on that simply because you know, it watched what she bought and she was probably buying you know, prenatal care, vitamins and stuff. But that article got shared around as the sign that big data was going to be negative. Target knows you're pregnant. What a horrible invasion of privacy. That title alone should, you know, make everyone's skin crawl. But that's the problem is that that shouldn't be the case. We think of there are so many opportunities to be using data and algorithms to see where disease outbreaks are going to occur or predict in the same way as what kind of conditions you might have so you can live a healthier life. And so I think it was then that we really thought, Okay, we need to come out and show the positive sides of this. Otherwise everyone's going to just run to the fear around what data science can do. We're interested on this podcast and people who've taken this leap to become problem solvers and to take on the biggest problems in the world. What made you take a leap to leave the private sector to start an organization with an ambitious goal. Well, I have to say it was a bit of an accident. Actually it was maybe twenty ten or eleven, and I had just coincidentally come out of school with a computer science and a statistics degree, which little did I know was going to become what would lead to the title data scientist. And I was working at the New York Times R and D Lab, and really what seemed obvious was the fact that we had all of this new digital technology, from cell phones that people were carrying around with them, to satellites launching in the air, to sends being put around the world, that we were digitizing our very existence. We were becoming a digital species. There was almost like a central nervous system to the world, and that meant that were these huge opportunities to learn from that to you know, have algorithms drive maybe our greatest human values. But the folks who really knew how to convert data into those actions. The data scientists were largely locked up in tech companies, and you know, I would actually go to hackathons, which are you know, like weekend events where technologists would get together and just work on whatever they thought was cool. And I would sit there and think, this is so interesting because you know, we're not at a company, we're not at our jobs. We're here on the weekend. You know, I'm sitting next to some machine learning engineer from Google and NASA scientist, and I'm like, this is great. We can make whatever we want. Like the world has just become so ripe for what's possible. And at the end of the day, the stuff that people made was just so unfulfilling. You know that someone had made like Twitter for pets, or had improved how you'd find local deals in your neighborhood, and so I just said, man, there's got to be something more we can do for society, or something more fulfilling really than this, as opposed to solving the problems of very well paid twenty somethings in the Bay Area, right, which is the parody, but that is a lot of the new companies you hear about are solving problems like how do you get your food delivered or god knows how to get cannabis delivered? You know when you when you could already buy it by walking around the corner. You're exactly right. We solve the problems that we ourselves have. And as you've pointed out, the tech community for better for worse, excused young male US. So, yeah, I just thought, you know, what would it take for to be applied to the social sector. Where are the people who are on the front lines of getting people food or clean water? And how could you apply it there? And so I just wanted that job myself. What didn't exist? So I just wrote to a couple of folks in the community here in New York and said, hey, you know, instead of going and building you know, a door dash competitor, could we, I don't know, work with the Red Cross US or Kiva who goes cash transfers to folks, and say what could we do with their data? What could we learn? What are the positive ways we could work together with them? And I thought people would just say, yeah, good idea, Jake, but no thanks. I kind of just buried the little sign up link for folks, and I was surprised to find that people started sharing around before I knew it. I came back to work the next time, hundreds of emails in my inbox from people not just in the city but around the world saying, though this is great, I want to get involved with data kind, I want to do data kind France. At one point, a few months into this, the White House called and said, hey, we're interested in big data initiatives. What's this thing? And you know, joke because I don't know, it's not really a thing, But to me it really tapped into an energy from both the technology side and the nonprofits and governments who are writing, who said, we're energetic to take on this new wave of this technology and figure out how could be applied. And so our job ever since has really just been trying to support that community, harness its energy, and be helpful in any way we can. Since you've been doing this, it's amazing how quickly attitudes have shifted around big data and algorithms. I mean, just think about Facebook, which even a few years ago was thought as a socially positive company. That was why part of why people went to work there, and in just a couple of years it's become something that people think is an overwhelmingly negative force. Are we're swinging too far in the other direction in our skepticism about what data is going to be used for? Well, I think there's a healthy reckoning on how we've been using data and technology in the past. You're right that in the last couple of years there was sort of unfettered techno optimism amongst a lot of the big companies and that this would just change everything and nothing could ever go wrong with social media and data. So I think there is an obviously very healthy reckoning of this, and we're starting to realize what the downsides could be. What your point I think is missing and we really need to get acclimated to, is where do we go from there? You know, is the idea that we're just going to put the genie back in the bottle, not use digital information in these ways, regulate all companies into existence. I'm in favor of, by the way, stronger regulation, for sure, But I think what we need now is more examples and more of a community of practice around what it looks like to use these technologies ethically. That's a big conversation obviously, that's in the space right now. You hear a lot about the ethics of data use, ethics of AI, but even then I find those conversations fairly academic. I think what we need are some more positive examples of how it can be applied and positive principles that we all agree to adhere to. And so the data kind that's something we're really working to try to demonstrate, is to say, yes, we need to protect ourselves, uphold our civil liberties through data. Make sure that we're not degrading human life with what's going on with data in the business world? And what does it look like when you want to use data and algorithms to predict, say, inclement weather that could wipe out a crop and that's critical to someone's sustenance in another part of the world. What's the good version of this? You know? How do you make sure that it's accountable to those folks? How do we make sure that everyone involved has some sense of what the algorithm is doing and how their data is being used. And I don't think we can move past that point just by talking about it. I think we need real concrete examples of data scientists, nonprofits, social organizations, constituents getting together to say, what does the good version of this look like a better version. I should say there was a positive example in the news recently with the prediction of the cyclone in South Asia that killed very few people, and in the world before big data, that same storm might have killed a lot of people through panic, through all sorts of consequences because people wouldn't have known it was coming. I mean, is that the kind of example we're talking about here? Something positive? I think that's exactly right. So at data Kind we team technologists like data scientists who want to volunteer their time alongside social change organizations, be they government agencies or nonprofits who have a pro social mission, might be able to use data and algorithms to do even more, and we together they collaborate and kind of codesign the solutions that they might foster a better world. So some examples that we've seen are exactly what you're talking about. There was a project that a group did as a water district in California, and the problem they faced was when drought season comes, you know, it's really hard to get water to folks. People don't have water. That's obviously problematic. You need drinking water and water to bathe, etc. But more than that, the cost of not getting them water is really high because the only way that they can get water to the places they don't have it is to actually take a dump truck, drive it up to some other reservoir, maybe over to Nevada, literally fill it by hand and drive it back. So you're also facing like huge environmental costs, huge energy costs. So they ask the question, you know, could we figure out a way to predict how much water demand there's going to be at a more granular level so we can really understand and ration more effectively. And so we team them up with some data scientists that come from everywhere from Netflix to environmental science organizations, and together they collected the data at almost a block by block level, and they built an algorithm that sort of takes that data in and continually gives updates. Does water district to say, hey, this is how much we think people are going to use. Here's how much they've already used. Tomorrow, you're probably going to see this, And they said, in the first year of using this, they saved over twenty five million dollars in addition to getting water to people much more effectively. So I think when you hear about cases like that those are the kinds of examples that we want to kind of platform and see more even the world where within the confines of social organization these data and algorithms that can really drive real effectiveness. Now your people are all doing this for good. We've all heard about the kinds of bias issues that have started to turn up with predictive algorithms of different kinds, and they seem to get embedded just because of the inherited unconscious biases of the people who write the algorithm. Absolutely, how do you avoid recapitulating that problem again with the projects you're working on? Such an awesome question, and I think just to comment on the challenge generally, I think you really nailed it there. That the challenge that we face is that humans have been collecting data from our activities that incorporate unconscious bias, and so if you then have a machine learn from it or you analyze it, you write replicating that. So, while I will not admit that we have a perfect solution, because I mean we're sort of talking about the challenge of bias and humanity, some of the things that we really focus on is the technology to us that we're building is secondary to the outcome for people. So, for example, it's not exciting to us to build an algorithm that helps a homeless shelter triage people to the right homeless shelters correctly just because it's a cool algorithm. We only care if at the end of the day, the ultimate success metric that you know, a wide range of inclusive folks are getting housing is achieved. So I want to say that first because I think one of the reasons we see some of these biased challenges rise up is that folks say, hey, the algorithm is doing something. It's doing a thing I want, like giving out sentences in courts or you know, policing folks, but without a question of and how is it biased? Towards the end, you know, what's it achieving. But the other thing we do is we work extremely closely with our NGEO partners who are on the ground and who understand a lot of those challenges. And so we'll actually do what we call a pre mortem some other companies do, which is before we even start a project, we'll say, okay, let's pretend we jump to the end. Well, you know, basic questions like how will this be maintained, who's actually going to use this tool at the end of the day. But then we'll also ask two questions, which is one, what's the worst that happens if we fail? So if you're relying on us to build, this is not something we would necessarily build. But let's say someone said, hey, we want a tool that predicts whether you have cancer or not. Okay, well that's pretty serious. And if we don't succeed, are you stuck because you really needed that and now your organization can't proceed. That's important to know. But then we also ask what's the worst that happens if we succeed? So who is this going to affect? How would you know that it's wrong? Right? Like, how would you know just because it's chugging away making predictions? Is it doing the right thing? Is it disenfranchising certain groups? Could somebody use it to intentionally target people who have cancer? We ask a lot of those questions, and what's really important us in that questioning is who has the power and agency to both understand the algorithm and change the algorithm Because in the current landscape, when tech companies build algorithms, it's not much you can do. But you know, I don't have enough agency to know how Facebook's news feed algorithm works, nor can I really affect it much? But that's not acceptable to me when you're bringing algorithms into the public good space and this is actually affecting folks lives. So those are some of the questions we ask up front and really try to be rigorous with our partners around oversight of and oftentimes that's enough for us to not take on a project. It's great that you're thinking steps ahead about these projects, and your own solvable is, ironically, to put yourself out of business is to create a world in which you don't need a data kind to point people towards positive uses of data. That's right, What would it take to make that happen? And I guess playing your chess game. What happens when that happens. The day we close our doors is the data. Every frontline social change organization has the capabilities to use data and AI the same way the big tech companies do ethically and capably. And so you know, our little slice of that today is to bridge the gap in getting the human capital, the talent, the data scientists AI engineers to social organizations. That sort of step one is to show people the art of the possible and really get some of those challenges solved. But what do it take to do that? Long runs to think about what are the problems and hurdles we're trying to overcome with that model today, and they are that in the social sector there isn't enough awareness about what the technology could do or where it would be applied. So we have to start with that, and I think now increasingly you're seeing more of more folks understanding that, more companies talking about doing data and AI for good. So I feel like there's some progress there, But if you go further, you have to think, well, how would a government or nonprofit get access to these resources in the long term, And there I think there's going to be a long term shift in getting funding to move towards nonprofits for things like data science and AI. You're going to need maybe consultancies that actually provide this service in the social sector. There's lots of different models for where that capacity could come from, but I think the biggest things that we need right now are that awareness of how could be used and then the I say, the funding for ngox to be able to hire a data sciences and incorporate them into the work they do. Now. When that happens, what happens. Oh, I mean, I'd love to say that all challenges that are stymied by not having data science and AI are solved live apply ever after. But actually, what I think my most ambitious hope for the world is that we could actually tip the balance a little bit to where the social sector is paving the path for how machine learning and AI could be used. I think we're so built into this default model that business and wealthy countries set the agenda and everyone else kind of struggles to catch up and imitate. We're talking about a technology that is so fundamental to humanity because it relies on data about us. When we talk about AI, it is like automating human processes that I don't think that's something that should be just a business application that is ported to the world. There should be a place for us to say, what does it look like when we apply the technology to the better angels of our nature? What is human based AI? What are the things we care about? And I can't think of any other place besides the social sector whose sole mandate is to look out for humanity. So my dream is when you bridge that gap, when that's there. You could actually have this voice from the social sector itself saying what it looks like to have human based ai Jick. Do you think about the training of data scientists. I sometimes think we're just missing the intersection between moral philosophy and computer science. You know, the people who are majoring in college and electronic engineering aren't reading much Kant, and the people who are reading Kant don't understand much about computer programming, you know, And in a way, the problem is that the people at these tech companies don't have a different kind of background in literature and philosophy and history to think through the implications of what they're building the way you clearly are thinking through those implications. I think it's a really great point that when wielding the technology, it's really important to have a very varied sense of skills somewhere in the conversation. And increasingly you're seeing data science and tech curricula incorporate ethics training into their courses, which I think is great. In the same way that I'm not a historian myself, I feel like physics went through this reckoning with the ethics of what was being built when they went from the joy of all energy and nuclear power to the realizations of the downsides of the nuclear bomb nuclear weapons. So I think you're going to see that similar shift, which is which is great, But you know, I think what your question raises actually a bigger point to me, which is who holds the responsibility for the ethical applications of this technology? And I'll just say, while I would love to see, you know, ethical code around data science, it's a lot of responsibility to say that engineer x it has come out of college engineering college for two years and is working at big tech company and gets asked by their boss to build something fairly benign, like I upgrade to their their GPS system that recommends routes you can walk that avoid crime ridden areas. I say, here's an algorith build that. Well, number one, that's not necessarily a bad thing to builds not like you know, it's not as black and white as some people may feel about building a weapon or something. But of course, if you sort of play the game through, if everyone were using an app that avoided crime ridden areas, probably end up with some sort of digital segregation. So number one, there's already long range effects that you'd have to anticipate. But more than that, It also relies on that, you know, second year engineer to say, hey boss, yeah, I'm not doing that. You know this is I'm quitting, which, given you know people's career paths and the money associate with these jobs, is a big ask. So I would say it's not just about the technologies. I think the question is, you know, how do we share that responsibility? Is it the technologist to make this call? Was it the manager said we want to build this feature? Was it the constituents would be affected by that? Is a government to come regulate. I don't think there's any one answer, but I do think the frame that people have I'm hearing more in the public right now around technologists need to know the ethics, I think is missing the bigger picture that that alone isn't the right responsibility model. In my mind. You have two very different ideas of capitalism, right. I mean, there's an older idea that government sets the rules, tells you what you can and can't do, and that businesses should obey the law and regulation but go be very free to do what they want. Within that, the newer model suggests that the businesses themselves have a higher degree of social responsibility, and it's not enough to follow the rules that they have to be thinking about outcomes. Look, I would love to live in a world where business and social outcome were somehow linked, where the fact that businesses were accountable somehow to at least not doing harm, if not improving human life. That would be a really great intersection. Call me a cynic, but we're not really currently set up for that. The incentives aren't there. In my mind, businesses are still held mostly to the bottom line, even though we are seeing some increased interest in social entrepreneurship, where businesses may have a double bottom line, one that's monetary and one that's social, or new structures like b corps that actually say, hey, we are committed to some social cause. But I think it's a lot to ask of a company. And as much as it's a nice idea of a future of capitalism, it's certainly not the rule or the law. And so I don't think that's going to be the sole model that brings us to a world of pro social technology and AI. If for no other reason then certain human needs are inherently cost ineffective, I would say to solve at least currently if people could cry those if every social problem were able to align perfectly with a business needs, be in great shape. But when it comes to housing the homeless or making sure that people have food to eat, that is a difficult challenge that I don't see an immediate market solution too, and so I don't think even the best intention companies could survive in a market based world trying to solve that problem. I mean, Google, which is still the first and best known data company essentially has held out this promise that we're going to make a lot of money using data commercially targeting advertising, but we're going to use a lot of what we make, or at least some of it in a kind of philanthropy. We're going to try to create some of the kinds of solutions you're talking about that aren't driven by the profit motive. Does that work look like? I said, One of the big challenges we face, I think in the social sector right now is the lack of funding for innovation for your technology. And so if company are going to offer that great netwin, do I believe that the world's biggest challenges will be solved on the you know, philanthropic efforts of large companies that I'm not so hopeful. I think there. I still wonder where are the folks for whom the mandate is solely pro social, you know, for governments or again nonprofits or civic organizations whose very guiding mission is to make sure that human prosperity is enhanced. There's a little bit more of a direct line there. And so that's why I think it has to be a combination of the two, and why we focus so much on saying instead of trying to bend the Googles of the world to you know, being in charge of clean water, which frankly I think is really not not the way you want to go. Where the you know, the clean water organizations of the world who just need that same technology to be ten hundred times more effective. What are some things listeners to this podcast might be able to do to work towards the kinds of solutions you're thinking about. Well, the great thing about this cross cutting technology is that everyone has a role to play in creating this future vision of more social and positive AI. Well, first, I would say, if you're a technologist who works with data and you want to give your time and energy back, come aboard. There's a whole movement of folks doing this work. Whether you want to come work with us at Data Kind and work on projects pro bono, or with many of the other organizations like Driven Data, Data Science for Social Good, CODE for America who take technologists and apply them to social problems, come aboard. There's no reason to wait. And increasing Link asked the company you work for if there's opportunities to give back, because we see more tech companies do that. But if you're not a data scientist, non data scientist, I would say, yeah, I have to first give a shout out to anyone of the funder or donor world. One of the big gaps here is that there isn't enough funding for technology and innovation in the social sector. So I've been very impressed with the efforts of Rockefeller Foundation and MasterCard Impact Fund and others who are giving big amounts of funding to data and AI and social good to bring it on. We need more of that for this happen. But very lastly, if not a data scientist and you're not a funder, I would say there's a huge opportunity to get involved in just understanding what this new technology can do. Ciicero had a quote that you should take an interest in politics, because politics is definitely going to take an interest in you. And I feel exactly the same about data and algorithms. They're going to take an interest in all of us. In fact, they're shaping our lives already today. Maybe the reason you're listening to this podcast is because an algorithm recommended it to you based on your previous listening habits. And so if these tools are going to be shaping and visibly shaping our decisions, then it's all the more incumbent on us as society to understand what the ramifications are, where it's showing up in society, and how we might have some agency over the role we want it to play. I think so much of the reason you hear so much negativity today is because we don't understand it well enough and we don't have any agency to change it. So our only options are to shrug and say, well, I guess that's going to be the way it is, or to rail against it and say this is bad. But if we could get to a place where we had call it algorithmic literacy. Not everyone needs to code, but if you just understand a little more about it, then I think we'd progress towards a society where we felt like we had a more control agency over how we work with the machines instead of against them. That's a great point. And I have to ask you for a reading recommendation. If people need to get educated, what should they read. What's a thing or two they should read to get more sophisticated about data. So the best thing I think you can read are some of the blogs that actually talk about the state of the space today, because it's changing so much that you know there's no one book that's going to capture it. Yeah. So some of the ones I love are the company O'Reilly O'Reilly dot com. They have a feature on data and AI that's a weekly newsletter that comes out talking about everything from the interesting innovations and AI to what kind of privacy concerns are in the space today, and it's very readable for a common audience. I think that's one of the most interesting ones. I would also read Data and Society's newsletter. They are a group here in New York who are really tackling the question of what does it mean to have data and algorithms in society. They have some really great accessible writing there The other thing I would say is if you have the privilege of living near a medium, miss or big city that has a meetup community. There are tons of data science AI meetups where people go and just talk about what's going on in the space. And I always recommend that people drop by at least one because if you see it and feel it and here people are talking about you don't have to understand, you know, if there's any math on the board, but just you almost immediately, it creates a states where people walk and go, oh, I actually see what this is all about. So I would say if you happen to be a checkout meetup, dot com or any of those communities. The data scientists AI folks are very friendly and I know you'll have a great time, if not an educational one. Terrific. Well, Jake Probi, thanks for joining us Unsolvable My pleasure. Thanks so much for having me reasons for hope all of this potential being harnessed to improve people's lives, the really big stuff. Although my ears certainly did prick up when Jake mentioned Twitter for pets, as did my dog's ears. She has been dying to get online and really drag other dogs anonymously, of course, but both myself and my dog are pleased to see what data Kind has actually managed to do so far, creating algorithms that have helped transport clean water more effectively, informed government policy that protects communities from corruption, and detected crop disease using satellite imagery. Jake and his team and all those volunteers are leveling the playing fields and you can help too. Read more about data Kind and how to get involved at Rockefella Foundation dot org. Slash solvable. Solvable is a collaboration between Pushkin Industries and the Rockefella Foundation, with production by Chalk and Blade. Pushkin's executive producer is Mia LaBelle. Engineering by Jason Gambrell and the fine folks at GSI Studios. Original music composed by Pascal Wise. Special thanks to Maggie Taylor, Heather Faine, Julia Barton, Carlie Migliori, Sheriff Vincent, Jacob Weisberg, and Malcolm Gladwell. You can learn more about solving today's biggest problems at Rockefella Foundation dot org. Slash Solvable. I'm Mave Higgins, Now go solve Itt