Speaker 1: Pushkin. I'm Maybe Higgins, and this is solvable Interviews with
Speaker 1: the world's most innovative thinkers who are working to solve
Speaker 1: the world's biggest problems. My solvable is that every frontline
Speaker 1: social organization is the ability to use data and AI
Speaker 1: same way, same capacity that the big tech companies do today.
Speaker 1: I want to see a world where the same algorithms
Speaker 1: that are routing your packages to you your house coming
Speaker 1: so efficiently because an AI figured out the best way
Speaker 1: to avoid traffic and weather are just as equally being
Speaker 1: applied to delivering a vaccine through an area before it spoils.
Speaker 1: That's Jake poor Away, the founder and CEO of nonprofit
Speaker 1: Data Kind. He's talking to Jacob Weisberg about how he's
Speaker 1: working to make that world a reality. The Rockefeller Foundation
Speaker 1: has thought about this too. More than two point five
Speaker 1: quintillion bytes of data are produced every day. That's one
Speaker 1: hundred trillion bytes. This abundance of data, combined with rapidly
Speaker 1: advancing analytics capabilities, could really improve the lives of billions
Speaker 1: of people around the world, but it's only living up
Speaker 1: to a fraction of that potential. While private sector businesses
Speaker 1: have been building and deploying data science capabilities for many
Speaker 1: years now. Most organizations in the nonprofit and civic and
Speaker 1: public sectors are way behind. Of course, they want to
Speaker 1: use the applied data to make their work go farther
Speaker 1: and faster and to help more people, but they don't
Speaker 1: often have the resources. I mean, put yourself in the
Speaker 1: shoes of a newly minted graduate. They're probably wearing tivas.
Speaker 1: Last year, the San Francisco Chronicle analyze glass door data
Speaker 1: of the starting salaries of some of the biggest tech
Speaker 1: companies in the Bay Area. They found out that tech
Speaker 1: pays even for the young and inexperienced. The average starting
Speaker 1: salary for a software engineer was almost ninety two thousand dollars.
Speaker 1: So there's the workers and then there's the technology itself.
Speaker 1: We know the power data science can have for social
Speaker 1: good because we've seen it in action. When mission driven
Speaker 1: organizations have the right talent and tools and knowledge, data
Speaker 1: science can generate real human impact, helping vulnerable families access
Speaker 1: public benefits, saving water and money during droughts, and saving
Speaker 1: time in resettling refugees so that they can find homes
Speaker 1: and jobs faster. Jake Borway works on this stuff every day.
Speaker 1: He's a machine learning and technology enthusiast who loves nothing
Speaker 1: more than seeing good values in data. In twenty eleven,
Speaker 1: he found a data Kind, bringing together leading data scientists
Speaker 1: with high impact social organizations to better collect, analyze, and
Speaker 1: visualized data in the service of humanity. Jake works to
Speaker 1: ensure organizations like the Red Cross have access to AI
Speaker 1: and data science that's as good as the access enjoyed
Speaker 1: by huge companies like Facebook. Data Kind has twenty thousand
Speaker 1: volunteers around the world, who he likens to mets on
Speaker 1: San Frontier, the doctors without Borders, except their data scientists
Speaker 1: working pro bono with leading social change organizations on all
Speaker 1: kinds of projects, including one that has data scientists from
Speaker 1: Netflix predicting water usage in a California neighborhood. It's fascinating,
Speaker 1: So enjoy this conversation and I'll talk to you after.
Speaker 1: What's the problem. In a nutshell, the problem is that
Speaker 1: digital technology and artificial intelligence have exploded over the last
Speaker 1: ten or fifteen years, which have created huge opportunities in
Speaker 1: the corporate space or in building new apps for society,
Speaker 1: but there's very little application of that to social sector causes.
Speaker 1: So we have this huge opportunity to use a revolutionary
Speaker 1: technology to predict the future of things, to understand our
Speaker 1: society better, to automate things that we either don't want
Speaker 1: to or couldn't do, And yet there's a huge potential
Speaker 1: loss in that it's very difficult to get that applied
Speaker 1: to pro social causes that we need. Jake is a
Speaker 1: data scientist. When did you start to see some of
Speaker 1: the downsides around big data? Really? The article that I
Speaker 1: used to point to is like the beginnings of the
Speaker 1: tide turning to the negative. Was the article that was
Speaker 1: titled very salaciously, Target Knows You're pregnant, And if you
Speaker 1: remember this one from twenty thirteen, but the basic idea
Speaker 1: was that someone had their daughter, that maybe sixteen seventeen
Speaker 1: year old daughter was receiving mailers from Target that said, Hey,
Speaker 1: we think you need to buy kupons for baby diapers
Speaker 1: or formula, and the dad called up, you know Target, all, Matt,
Speaker 1: So what are you sending me all my daughter all
Speaker 1: these deals for having babies. She's not pregnant, Like, why
Speaker 1: are you trying to get her to become pregnant? And
Speaker 1: the person on the other end of the line, of
Speaker 1: course didn't know what was happening, because you know, the
Speaker 1: algorithms just send you what they think you're going to
Speaker 1: buy based on other stuff you've bought, and it's He
Speaker 1: called back later, kind of shame facedly and said, you know,
Speaker 1: I talked to my daughter and actually she is pregnant,
Speaker 1: and you know, the data had picked up on that
Speaker 1: simply because you know, it watched what she bought and
Speaker 1: she was probably buying you know, prenatal care, vitamins and stuff.
Speaker 1: But that article got shared around as the sign that
Speaker 1: big data was going to be negative. Target knows you're pregnant.
Speaker 1: What a horrible invasion of privacy. That title alone should,
Speaker 1: you know, make everyone's skin crawl. But that's the problem
Speaker 1: is that that shouldn't be the case. We think of
Speaker 1: there are so many opportunities to be using data and
Speaker 1: algorithms to see where disease outbreaks are going to occur
Speaker 1: or predict in the same way as what kind of
Speaker 1: conditions you might have so you can live a healthier life.
Speaker 1: And so I think it was then that we really thought, Okay,
Speaker 1: we need to come out and show the positive sides
Speaker 1: of this. Otherwise everyone's going to just run to the
Speaker 1: fear around what data science can do. We're interested on
Speaker 1: this podcast and people who've taken this leap to become
Speaker 1: problem solvers and to take on the biggest problems in
Speaker 1: the world. What made you take a leap to leave
Speaker 1: the private sector to start an organization with an ambitious goal. Well,
Speaker 1: I have to say it was a bit of an accident.
Speaker 1: Actually it was maybe twenty ten or eleven, and I
Speaker 1: had just coincidentally come out of school with a computer
Speaker 1: science and a statistics degree, which little did I know
Speaker 1: was going to become what would lead to the title
Speaker 1: data scientist. And I was working at the New York
Speaker 1: Times R and D Lab, and really what seemed obvious
Speaker 1: was the fact that we had all of this new
Speaker 1: digital technology, from cell phones that people were carrying around
Speaker 1: with them, to satellites launching in the air, to sends
Speaker 1: being put around the world, that we were digitizing our
Speaker 1: very existence. We were becoming a digital species. There was
Speaker 1: almost like a central nervous system to the world, and
Speaker 1: that meant that were these huge opportunities to learn from
Speaker 1: that to you know, have algorithms drive maybe our greatest
Speaker 1: human values. But the folks who really knew how to
Speaker 1: convert data into those actions. The data scientists were largely
Speaker 1: locked up in tech companies, and you know, I would
Speaker 1: actually go to hackathons, which are you know, like weekend
Speaker 1: events where technologists would get together and just work on
Speaker 1: whatever they thought was cool. And I would sit there
Speaker 1: and think, this is so interesting because you know, we're
Speaker 1: not at a company, we're not at our jobs. We're
Speaker 1: here on the weekend. You know, I'm sitting next to
Speaker 1: some machine learning engineer from Google and NASA scientist, and
Speaker 1: I'm like, this is great. We can make whatever we want.
Speaker 1: Like the world has just become so ripe for what's possible.
Speaker 1: And at the end of the day, the stuff that
Speaker 1: people made was just so unfulfilling. You know that someone
Speaker 1: had made like Twitter for pets, or had improved how
Speaker 1: you'd find local deals in your neighborhood, and so I
Speaker 1: just said, man, there's got to be something more we
Speaker 1: can do for society, or something more fulfilling really than this,
Speaker 1: as opposed to solving the problems of very well paid
Speaker 1: twenty somethings in the Bay Area, right, which is the parody,
Speaker 1: but that is a lot of the new companies you
Speaker 1: hear about are solving problems like how do you get
Speaker 1: your food delivered or god knows how to get cannabis delivered?
Speaker 1: You know when you when you could already buy it
Speaker 1: by walking around the corner. You're exactly right. We solve
Speaker 1: the problems that we ourselves have. And as you've pointed out,
Speaker 1: the tech community for better for worse, excused young male US. So, yeah,
Speaker 1: I just thought, you know, what would it take for
Speaker 1: to be applied to the social sector. Where are the
Speaker 1: people who are on the front lines of getting people
Speaker 1: food or clean water? And how could you apply it there?
Speaker 1: And so I just wanted that job myself. What didn't exist?
Speaker 1: So I just wrote to a couple of folks in
Speaker 1: the community here in New York and said, hey, you know,
Speaker 1: instead of going and building you know, a door dash competitor,
Speaker 1: could we, I don't know, work with the Red Cross
Speaker 1: US or Kiva who goes cash transfers to folks, and
Speaker 1: say what could we do with their data? What could
Speaker 1: we learn? What are the positive ways we could work
Speaker 1: together with them? And I thought people would just say, yeah,
Speaker 1: good idea, Jake, but no thanks. I kind of just
Speaker 1: buried the little sign up link for folks, and I
Speaker 1: was surprised to find that people started sharing around before
Speaker 1: I knew it. I came back to work the next time,
Speaker 1: hundreds of emails in my inbox from people not just
Speaker 1: in the city but around the world saying, though this
Speaker 1: is great, I want to get involved with data kind,
Speaker 1: I want to do data kind France. At one point,
Speaker 1: a few months into this, the White House called and said, hey,
Speaker 1: we're interested in big data initiatives. What's this thing? And
Speaker 1: you know, joke because I don't know, it's not really
Speaker 1: a thing, But to me it really tapped into an
Speaker 1: energy from both the technology side and the nonprofits and
Speaker 1: governments who are writing, who said, we're energetic to take
Speaker 1: on this new wave of this technology and figure out
Speaker 1: how could be applied. And so our job ever since
Speaker 1: has really just been trying to support that community, harness
Speaker 1: its energy, and be helpful in any way we can.
Speaker 1: Since you've been doing this, it's amazing how quickly attitudes
Speaker 1: have shifted around big data and algorithms. I mean, just
Speaker 1: think about Facebook, which even a few years ago was
Speaker 1: thought as a socially positive company. That was why part
Speaker 1: of why people went to work there, and in just
Speaker 1: a couple of years it's become something that people think
Speaker 1: is an overwhelmingly negative force. Are we're swinging too far
Speaker 1: in the other direction in our skepticism about what data
Speaker 1: is going to be used for? Well, I think there's
Speaker 1: a healthy reckoning on how we've been using data and
Speaker 1: technology in the past. You're right that in the last
Speaker 1: couple of years there was sort of unfettered techno optimism
Speaker 1: amongst a lot of the big companies and that this
Speaker 1: would just change everything and nothing could ever go wrong
Speaker 1: with social media and data. So I think there is
Speaker 1: an obviously very healthy reckoning of this, and we're starting
Speaker 1: to realize what the downsides could be. What your point
Speaker 1: I think is missing and we really need to get
Speaker 1: acclimated to, is where do we go from there? You know,
Speaker 1: is the idea that we're just going to put the
Speaker 1: genie back in the bottle, not use digital information in
Speaker 1: these ways, regulate all companies into existence. I'm in favor of,
Speaker 1: by the way, stronger regulation, for sure, But I think
Speaker 1: what we need now is more examples and more of
Speaker 1: a community of practice around what it looks like to
Speaker 1: use these technologies ethically. That's a big conversation obviously, that's
Speaker 1: in the space right now. You hear a lot about
Speaker 1: the ethics of data use, ethics of AI, but even
Speaker 1: then I find those conversations fairly academic. I think what
Speaker 1: we need are some more positive examples of how it
Speaker 1: can be applied and positive principles that we all agree
Speaker 1: to adhere to. And so the data kind that's something
Speaker 1: we're really working to try to demonstrate, is to say, yes,
Speaker 1: we need to protect ourselves, uphold our civil liberties through data.
Speaker 1: Make sure that we're not degrading human life with what's
Speaker 1: going on with data in the business world? And what
Speaker 1: does it look like when you want to use data
Speaker 1: and algorithms to predict, say, inclement weather that could wipe
Speaker 1: out a crop and that's critical to someone's sustenance in
Speaker 1: another part of the world. What's the good version of this?
Speaker 1: You know? How do you make sure that it's accountable
Speaker 1: to those folks? How do we make sure that everyone
Speaker 1: involved has some sense of what the algorithm is doing
Speaker 1: and how their data is being used. And I don't
Speaker 1: think we can move past that point just by talking
Speaker 1: about it. I think we need real concrete examples of
Speaker 1: data scientists, nonprofits, social organizations, constituents getting together to say,
Speaker 1: what does the good version of this look like a
Speaker 1: better version. I should say there was a positive example
Speaker 1: in the news recently with the prediction of the cyclone
Speaker 1: in South Asia that killed very few people, and in
Speaker 1: the world before big data, that same storm might have
Speaker 1: killed a lot of people through panic, through all sorts
Speaker 1: of consequences because people wouldn't have known it was coming.
Speaker 1: I mean, is that the kind of example we're talking
Speaker 1: about here? Something positive? I think that's exactly right. So
Speaker 1: at data Kind we team technologists like data scientists who
Speaker 1: want to volunteer their time alongside social change organizations, be
Speaker 1: they government agencies or nonprofits who have a pro social mission,
Speaker 1: might be able to use data and algorithms to do
Speaker 1: even more, and we together they collaborate and kind of
Speaker 1: codesign the solutions that they might foster a better world.
Speaker 1: So some examples that we've seen are exactly what you're
Speaker 1: talking about. There was a project that a group did
Speaker 1: as a water district in California, and the problem they
Speaker 1: faced was when drought season comes, you know, it's really
Speaker 1: hard to get water to folks. People don't have water.
Speaker 1: That's obviously problematic. You need drinking water and water to bathe, etc.
Speaker 1: But more than that, the cost of not getting them
Speaker 1: water is really high because the only way that they
Speaker 1: can get water to the places they don't have it
Speaker 1: is to actually take a dump truck, drive it up
Speaker 1: to some other reservoir, maybe over to Nevada, literally fill
Speaker 1: it by hand and drive it back. So you're also
Speaker 1: facing like huge environmental costs, huge energy costs. So they
Speaker 1: ask the question, you know, could we figure out a
Speaker 1: way to predict how much water demand there's going to
Speaker 1: be at a more granular level so we can really
Speaker 1: understand and ration more effectively. And so we team them
Speaker 1: up with some data scientists that come from everywhere from
Speaker 1: Netflix to environmental science organizations, and together they collected the
Speaker 1: data at almost a block by block level, and they
Speaker 1: built an algorithm that sort of takes that data in
Speaker 1: and continually gives updates. Does water district to say, hey,
Speaker 1: this is how much we think people are going to use.
Speaker 1: Here's how much they've already used. Tomorrow, you're probably going
Speaker 1: to see this, And they said, in the first year
Speaker 1: of using this, they saved over twenty five million dollars
Speaker 1: in addition to getting water to people much more effectively.
Speaker 1: So I think when you hear about cases like that
Speaker 1: those are the kinds of examples that we want to
Speaker 1: kind of platform and see more even the world where
Speaker 1: within the confines of social organization these data and algorithms
Speaker 1: that can really drive real effectiveness. Now your people are
Speaker 1: all doing this for good. We've all heard about the
Speaker 1: kinds of bias issues that have started to turn up
Speaker 1: with predictive algorithms of different kinds, and they seem to
Speaker 1: get embedded just because of the inherited unconscious biases of
Speaker 1: the people who write the algorithm. Absolutely, how do you
Speaker 1: avoid recapitulating that problem again with the projects you're working on?
Speaker 1: Such an awesome question, and I think just to comment
Speaker 1: on the challenge generally, I think you really nailed it there.
Speaker 1: That the challenge that we face is that humans have
Speaker 1: been collecting data from our activities that incorporate unconscious bias,
Speaker 1: and so if you then have a machine learn from
Speaker 1: it or you analyze it, you write replicating that. So,
Speaker 1: while I will not admit that we have a perfect solution,
Speaker 1: because I mean we're sort of talking about the challenge
Speaker 1: of bias and humanity, some of the things that we
Speaker 1: really focus on is the technology to us that we're
Speaker 1: building is secondary to the outcome for people. So, for example,
Speaker 1: it's not exciting to us to build an algorithm that
Speaker 1: helps a homeless shelter triage people to the right homeless
Speaker 1: shelters correctly just because it's a cool algorithm. We only
Speaker 1: care if at the end of the day, the ultimate
Speaker 1: success metric that you know, a wide range of inclusive
Speaker 1: folks are getting housing is achieved. So I want to
Speaker 1: say that first because I think one of the reasons
Speaker 1: we see some of these biased challenges rise up is
Speaker 1: that folks say, hey, the algorithm is doing something. It's
Speaker 1: doing a thing I want, like giving out sentences in
Speaker 1: courts or you know, policing folks, but without a question
Speaker 1: of and how is it biased? Towards the end, you know,
Speaker 1: what's it achieving. But the other thing we do is
Speaker 1: we work extremely closely with our NGEO partners who are
Speaker 1: on the ground and who understand a lot of those challenges.
Speaker 1: And so we'll actually do what we call a pre
Speaker 1: mortem some other companies do, which is before we even
Speaker 1: start a project, we'll say, okay, let's pretend we jump
Speaker 1: to the end. Well, you know, basic questions like how
Speaker 1: will this be maintained, who's actually going to use this
Speaker 1: tool at the end of the day. But then we'll
Speaker 1: also ask two questions, which is one, what's the worst
Speaker 1: that happens if we fail? So if you're relying on
Speaker 1: us to build, this is not something we would necessarily build.
Speaker 1: But let's say someone said, hey, we want a tool
Speaker 1: that predicts whether you have cancer or not. Okay, well
Speaker 1: that's pretty serious. And if we don't succeed, are you
Speaker 1: stuck because you really needed that and now your organization
Speaker 1: can't proceed. That's important to know. But then we also
Speaker 1: ask what's the worst that happens if we succeed? So
Speaker 1: who is this going to affect? How would you know
Speaker 1: that it's wrong? Right? Like, how would you know just
Speaker 1: because it's chugging away making predictions? Is it doing the
Speaker 1: right thing? Is it disenfranchising certain groups? Could somebody use
Speaker 1: it to intentionally target people who have cancer? We ask
Speaker 1: a lot of those questions, and what's really important us
Speaker 1: in that questioning is who has the power and agency
Speaker 1: to both understand the algorithm and change the algorithm Because
Speaker 1: in the current landscape, when tech companies build algorithms, it's
Speaker 1: not much you can do. But you know, I don't
Speaker 1: have enough agency to know how Facebook's news feed algorithm works,
Speaker 1: nor can I really affect it much? But that's not
Speaker 1: acceptable to me when you're bringing algorithms into the public
Speaker 1: good space and this is actually affecting folks lives. So
Speaker 1: those are some of the questions we ask up front
Speaker 1: and really try to be rigorous with our partners around
Speaker 1: oversight of and oftentimes that's enough for us to not
Speaker 1: take on a project. It's great that you're thinking steps
Speaker 1: ahead about these projects, and your own solvable is, ironically,
Speaker 1: to put yourself out of business is to create a
Speaker 1: world in which you don't need a data kind to
Speaker 1: point people towards positive uses of data. That's right, What
Speaker 1: would it take to make that happen? And I guess
Speaker 1: playing your chess game. What happens when that happens. The
Speaker 1: day we close our doors is the data. Every frontline
Speaker 1: social change organization has the capabilities to use data and
Speaker 1: AI the same way the big tech companies do ethically
Speaker 1: and capably. And so you know, our little slice of
Speaker 1: that today is to bridge the gap in getting the
Speaker 1: human capital, the talent, the data scientists AI engineers to
Speaker 1: social organizations. That sort of step one is to show
Speaker 1: people the art of the possible and really get some
Speaker 1: of those challenges solved. But what do it take to
Speaker 1: do that? Long runs to think about what are the
Speaker 1: problems and hurdles we're trying to overcome with that model today,
Speaker 1: and they are that in the social sector there isn't
Speaker 1: enough awareness about what the technology could do or where
Speaker 1: it would be applied. So we have to start with that,
Speaker 1: and I think now increasingly you're seeing more of more
Speaker 1: folks understanding that, more companies talking about doing data and
Speaker 1: AI for good. So I feel like there's some progress there,
Speaker 1: But if you go further, you have to think, well,
Speaker 1: how would a government or nonprofit get access to these
Speaker 1: resources in the long term, And there I think there's
Speaker 1: going to be a long term shift in getting funding
Speaker 1: to move towards nonprofits for things like data science and AI.
Speaker 1: You're going to need maybe consultancies that actually provide this
Speaker 1: service in the social sector. There's lots of different models
Speaker 1: for where that capacity could come from, but I think
Speaker 1: the biggest things that we need right now are that
Speaker 1: awareness of how could be used and then the I say,
Speaker 1: the funding for ngox to be able to hire a
Speaker 1: data sciences and incorporate them into the work they do. Now.
Speaker 1: When that happens, what happens. Oh, I mean, I'd love
Speaker 1: to say that all challenges that are stymied by not
Speaker 1: having data science and AI are solved live apply ever after.
Speaker 1: But actually, what I think my most ambitious hope for
Speaker 1: the world is that we could actually tip the balance
Speaker 1: a little bit to where the social sector is paving
Speaker 1: the path for how machine learning and AI could be used.
Speaker 1: I think we're so built into this default model that
Speaker 1: business and wealthy countries set the agenda and everyone else
Speaker 1: kind of struggles to catch up and imitate. We're talking
Speaker 1: about a technology that is so fundamental to humanity because
Speaker 1: it relies on data about us. When we talk about AI,
Speaker 1: it is like automating human processes that I don't think
Speaker 1: that's something that should be just a business application that
Speaker 1: is ported to the world. There should be a place
Speaker 1: for us to say, what does it look like when
Speaker 1: we apply the technology to the better angels of our nature?
Speaker 1: What is human based AI? What are the things we
Speaker 1: care about? And I can't think of any other place
Speaker 1: besides the social sector whose sole mandate is to look
Speaker 1: out for humanity. So my dream is when you bridge
Speaker 1: that gap, when that's there. You could actually have this
Speaker 1: voice from the social sector itself saying what it looks
Speaker 1: like to have human based ai Jick. Do you think
Speaker 1: about the training of data scientists. I sometimes think we're
Speaker 1: just missing the intersection between moral philosophy and computer science.
Speaker 1: You know, the people who are majoring in college and
Speaker 1: electronic engineering aren't reading much Kant, and the people who
Speaker 1: are reading Kant don't understand much about computer programming, you know,
Speaker 1: And in a way, the problem is that the people
Speaker 1: at these tech companies don't have a different kind of
Speaker 1: background in literature and philosophy and history to think through
Speaker 1: the implications of what they're building the way you clearly
Speaker 1: are thinking through those implications. I think it's a really
Speaker 1: great point that when wielding the technology, it's really important
Speaker 1: to have a very varied sense of skills somewhere in
Speaker 1: the conversation. And increasingly you're seeing data science and tech
Speaker 1: curricula incorporate ethics training into their courses, which I think
Speaker 1: is great. In the same way that I'm not a
Speaker 1: historian myself, I feel like physics went through this reckoning
Speaker 1: with the ethics of what was being built when they
Speaker 1: went from the joy of all energy and nuclear power
Speaker 1: to the realizations of the downsides of the nuclear bomb
Speaker 1: nuclear weapons. So I think you're going to see that
Speaker 1: similar shift, which is which is great, But you know,
Speaker 1: I think what your question raises actually a bigger point
Speaker 1: to me, which is who holds the responsibility for the
Speaker 1: ethical applications of this technology? And I'll just say, while
Speaker 1: I would love to see, you know, ethical code around
Speaker 1: data science, it's a lot of responsibility to say that
Speaker 1: engineer x it has come out of college engineering college
Speaker 1: for two years and is working at big tech company
Speaker 1: and gets asked by their boss to build something fairly benign,
Speaker 1: like I upgrade to their their GPS system that recommends
Speaker 1: routes you can walk that avoid crime ridden areas. I say,
Speaker 1: here's an algorith build that. Well, number one, that's not
Speaker 1: necessarily a bad thing to builds not like you know,
Speaker 1: it's not as black and white as some people may
Speaker 1: feel about building a weapon or something. But of course,
Speaker 1: if you sort of play the game through, if everyone
Speaker 1: were using an app that avoided crime ridden areas, probably
Speaker 1: end up with some sort of digital segregation. So number one,
Speaker 1: there's already long range effects that you'd have to anticipate.
Speaker 1: But more than that, It also relies on that, you know,
Speaker 1: second year engineer to say, hey boss, yeah, I'm not
Speaker 1: doing that. You know this is I'm quitting, which, given
Speaker 1: you know people's career paths and the money associate with
Speaker 1: these jobs, is a big ask. So I would say
Speaker 1: it's not just about the technologies. I think the question is,
Speaker 1: you know, how do we share that responsibility? Is it
Speaker 1: the technologist to make this call? Was it the manager
Speaker 1: said we want to build this feature? Was it the
Speaker 1: constituents would be affected by that? Is a government to
Speaker 1: come regulate. I don't think there's any one answer, but
Speaker 1: I do think the frame that people have I'm hearing
Speaker 1: more in the public right now around technologists need to
Speaker 1: know the ethics, I think is missing the bigger picture
Speaker 1: that that alone isn't the right responsibility model. In my mind.
Speaker 1: You have two very different ideas of capitalism, right. I mean,
Speaker 1: there's an older idea that government sets the rules, tells
Speaker 1: you what you can and can't do, and that businesses
Speaker 1: should obey the law and regulation but go be very
Speaker 1: free to do what they want. Within that, the newer
Speaker 1: model suggests that the businesses themselves have a higher degree
Speaker 1: of social responsibility, and it's not enough to follow the
Speaker 1: rules that they have to be thinking about outcomes. Look,
Speaker 1: I would love to live in a world where business
Speaker 1: and social outcome were somehow linked, where the fact that
Speaker 1: businesses were accountable somehow to at least not doing harm,
Speaker 1: if not improving human life. That would be a really
Speaker 1: great intersection. Call me a cynic, but we're not really
Speaker 1: currently set up for that. The incentives aren't there. In
Speaker 1: my mind, businesses are still held mostly to the bottom line,
Speaker 1: even though we are seeing some increased interest in social entrepreneurship,
Speaker 1: where businesses may have a double bottom line, one that's
Speaker 1: monetary and one that's social, or new structures like b
Speaker 1: corps that actually say, hey, we are committed to some
Speaker 1: social cause. But I think it's a lot to ask
Speaker 1: of a company. And as much as it's a nice
Speaker 1: idea of a future of capitalism, it's certainly not the
Speaker 1: rule or the law. And so I don't think that's
Speaker 1: going to be the sole model that brings us to
Speaker 1: a world of pro social technology and AI. If for
Speaker 1: no other reason then certain human needs are inherently cost ineffective,
Speaker 1: I would say to solve at least currently if people
Speaker 1: could cry those if every social problem were able to
Speaker 1: align perfectly with a business needs, be in great shape.
Speaker 1: But when it comes to housing the homeless or making
Speaker 1: sure that people have food to eat, that is a
Speaker 1: difficult challenge that I don't see an immediate market solution too,
Speaker 1: and so I don't think even the best intention companies
Speaker 1: could survive in a market based world trying to solve
Speaker 1: that problem. I mean, Google, which is still the first
Speaker 1: and best known data company essentially has held out this
Speaker 1: promise that we're going to make a lot of money
Speaker 1: using data commercially targeting advertising, but we're going to use
Speaker 1: a lot of what we make, or at least some
Speaker 1: of it in a kind of philanthropy. We're going to
Speaker 1: try to create some of the kinds of solutions you're
Speaker 1: talking about that aren't driven by the profit motive. Does
Speaker 1: that work look like? I said, One of the big
Speaker 1: challenges we face, I think in the social sector right
Speaker 1: now is the lack of funding for innovation for your technology.
Speaker 1: And so if company are going to offer that great netwin,
Speaker 1: do I believe that the world's biggest challenges will be
Speaker 1: solved on the you know, philanthropic efforts of large companies
Speaker 1: that I'm not so hopeful. I think there. I still
Speaker 1: wonder where are the folks for whom the mandate is
Speaker 1: solely pro social, you know, for governments or again nonprofits
Speaker 1: or civic organizations whose very guiding mission is to make
Speaker 1: sure that human prosperity is enhanced. There's a little bit
Speaker 1: more of a direct line there. And so that's why
Speaker 1: I think it has to be a combination of the two,
Speaker 1: and why we focus so much on saying instead of
Speaker 1: trying to bend the Googles of the world to you know,
Speaker 1: being in charge of clean water, which frankly I think
Speaker 1: is really not not the way you want to go.
Speaker 1: Where the you know, the clean water organizations of the
Speaker 1: world who just need that same technology to be ten
Speaker 1: hundred times more effective. What are some things listeners to
Speaker 1: this podcast might be able to do to work towards
Speaker 1: the kinds of solutions you're thinking about. Well, the great
Speaker 1: thing about this cross cutting technology is that everyone has
Speaker 1: a role to play in creating this future vision of
Speaker 1: more social and positive AI. Well, first, I would say,
Speaker 1: if you're a technologist who works with data and you
Speaker 1: want to give your time and energy back, come aboard.
Speaker 1: There's a whole movement of folks doing this work. Whether
Speaker 1: you want to come work with us at Data Kind
Speaker 1: and work on projects pro bono, or with many of
Speaker 1: the other organizations like Driven Data, Data Science for Social Good,
Speaker 1: CODE for America who take technologists and apply them to
Speaker 1: social problems, come aboard. There's no reason to wait. And
Speaker 1: increasing Link asked the company you work for if there's
Speaker 1: opportunities to give back, because we see more tech companies
Speaker 1: do that. But if you're not a data scientist, non
Speaker 1: data scientist, I would say, yeah, I have to first
Speaker 1: give a shout out to anyone of the funder or
Speaker 1: donor world. One of the big gaps here is that
Speaker 1: there isn't enough funding for technology and innovation in the
Speaker 1: social sector. So I've been very impressed with the efforts
Speaker 1: of Rockefeller Foundation and MasterCard Impact Fund and others who
Speaker 1: are giving big amounts of funding to data and AI
Speaker 1: and social good to bring it on. We need more
Speaker 1: of that for this happen. But very lastly, if not
Speaker 1: a data scientist and you're not a funder, I would
Speaker 1: say there's a huge opportunity to get involved in just
Speaker 1: understanding what this new technology can do. Ciicero had a
Speaker 1: quote that you should take an interest in politics, because
Speaker 1: politics is definitely going to take an interest in you.
Speaker 1: And I feel exactly the same about data and algorithms.
Speaker 1: They're going to take an interest in all of us.
Speaker 1: In fact, they're shaping our lives already today. Maybe the
Speaker 1: reason you're listening to this podcast is because an algorithm
Speaker 1: recommended it to you based on your previous listening habits.
Speaker 1: And so if these tools are going to be shaping
Speaker 1: and visibly shaping our decisions, then it's all the more
Speaker 1: incumbent on us as society to understand what the ramifications are,
Speaker 1: where it's showing up in society, and how we might
Speaker 1: have some agency over the role we want it to play.
Speaker 1: I think so much of the reason you hear so
Speaker 1: much negativity today is because we don't understand it well
Speaker 1: enough and we don't have any agency to change it.
Speaker 1: So our only options are to shrug and say, well,
Speaker 1: I guess that's going to be the way it is,
Speaker 1: or to rail against it and say this is bad.
Speaker 1: But if we could get to a place where we
Speaker 1: had call it algorithmic literacy. Not everyone needs to code,
Speaker 1: but if you just understand a little more about it,
Speaker 1: then I think we'd progress towards a society where we
Speaker 1: felt like we had a more control agency over how
Speaker 1: we work with the machines instead of against them. That's
Speaker 1: a great point. And I have to ask you for
Speaker 1: a reading recommendation. If people need to get educated, what
Speaker 1: should they read. What's a thing or two they should
Speaker 1: read to get more sophisticated about data. So the best
Speaker 1: thing I think you can read are some of the
Speaker 1: blogs that actually talk about the state of the space today,
Speaker 1: because it's changing so much that you know there's no
Speaker 1: one book that's going to capture it. Yeah. So some
Speaker 1: of the ones I love are the company O'Reilly O'Reilly
Speaker 1: dot com. They have a feature on data and AI
Speaker 1: that's a weekly newsletter that comes out talking about everything
Speaker 1: from the interesting innovations and AI to what kind of
Speaker 1: privacy concerns are in the space today, and it's very
Speaker 1: readable for a common audience. I think that's one of
Speaker 1: the most interesting ones. I would also read Data and
Speaker 1: Society's newsletter. They are a group here in New York
Speaker 1: who are really tackling the question of what does it
Speaker 1: mean to have data and algorithms in society. They have
Speaker 1: some really great accessible writing there The other thing I
Speaker 1: would say is if you have the privilege of living
Speaker 1: near a medium, miss or big city that has a
Speaker 1: meetup community. There are tons of data science AI meetups
Speaker 1: where people go and just talk about what's going on
Speaker 1: in the space. And I always recommend that people drop
Speaker 1: by at least one because if you see it and
Speaker 1: feel it and here people are talking about you don't
Speaker 1: have to understand, you know, if there's any math on
Speaker 1: the board, but just you almost immediately, it creates a
Speaker 1: states where people walk and go, oh, I actually see
Speaker 1: what this is all about. So I would say if
Speaker 1: you happen to be a checkout meetup, dot com or
Speaker 1: any of those communities. The data scientists AI folks are
Speaker 1: very friendly and I know you'll have a great time,
Speaker 1: if not an educational one. Terrific. Well, Jake Probi, thanks
Speaker 1: for joining us Unsolvable My pleasure. Thanks so much for
Speaker 1: having me reasons for hope all of this potential being
Speaker 1: harnessed to improve people's lives, the really big stuff. Although
Speaker 1: my ears certainly did prick up when Jake mentioned Twitter
Speaker 1: for pets, as did my dog's ears. She has been
Speaker 1: dying to get online and really drag other dogs anonymously,
Speaker 1: of course, but both myself and my dog are pleased
Speaker 1: to see what data Kind has actually managed to do
Speaker 1: so far, creating algorithms that have helped transport clean water
Speaker 1: more effectively, informed government policy that protects communities from corruption,
Speaker 1: and detected crop disease using satellite imagery. Jake and his
Speaker 1: team and all those volunteers are leveling the playing fields
Speaker 1: and you can help too. Read more about data Kind
Speaker 1: and how to get involved at Rockefella Foundation dot org.
Speaker 1: Slash solvable. Solvable is a collaboration between Pushkin Industries and
Speaker 1: the Rockefella Foundation, with production by Chalk and Blade. Pushkin's
Speaker 1: executive producer is Mia LaBelle. Engineering by Jason Gambrell and
Speaker 1: the fine folks at GSI Studios. Original music composed by
Speaker 1: Pascal Wise. Special thanks to Maggie Taylor, Heather Faine, Julia Barton,
Speaker 1: Carlie Migliori, Sheriff Vincent, Jacob Weisberg, and Malcolm Gladwell. You
Speaker 1: can learn more about solving today's biggest problems at Rockefella
Speaker 1: Foundation dot org. Slash Solvable. I'm Mave Higgins, Now go
Speaker 1: solve Itt