Speaker 1: Bushkin, This is solvable. I'm Jacob Weisberg. Typically, if a
Speaker 1: car crashes because there's say a faulty drive train, we
Speaker 1: can point to the engineering and say there's a problem
Speaker 1: with this system. With these adaptive systems, they're reacting and
Speaker 1: learning and responding to human society and human behavior, and
Speaker 1: we're still developing the scientific tools to understand what it
Speaker 1: means to have those feedback loops. Algorithms are adaptive systems.
Speaker 1: They're pieces of computer code that shape many aspects of
Speaker 1: our digital lives. They're closely guarded trade secrets and powerful tools,
Speaker 1: and they're regularly blamed for amplifying cultural and political divisions.
Speaker 1: We often hear technologists say we couldn't have known, and
Speaker 1: the idea that they haven't turned those lenses on questions
Speaker 1: impacting the common good. It's a scandal if they haven't
Speaker 1: ask the question. It's a scandal if they've asked it
Speaker 1: and they're not telling us what they found. This is
Speaker 1: a fourth chapter in our Solvable series examining solutions for
Speaker 1: America's polarization problem. Today we're talking about social media algorithms
Speaker 1: and how to deal with them. You can think of
Speaker 1: social media companies as fancy restaurants. The cooks behind the
Speaker 1: most successful one often don't want to reveal their recipes,
Speaker 1: but customers have a right to know what they're eating.
Speaker 1: It turns out we've been down this road before. The
Speaker 1: Good Housekeeping labs started just around the turn of the century.
Speaker 1: People were concerned about what was in their food what
Speaker 1: was in others. This was before the creation of the FDA,
Speaker 1: and so people subscribed to Good Housekeeping. Those labs would
Speaker 1: test common products and tell people if they were safe
Speaker 1: or not. Ultimately, the federal government stepped in to regulate
Speaker 1: food safety, including disclosure requirements around ingredients, nutrients, and calories. Similarly,
Speaker 1: establishing algorithmic safety and accountability will take a variety of players.
Speaker 1: I want to live in a world where digital power
Speaker 1: is both guided by evidence and accountable to the public.
Speaker 1: Nathan Mathias teaches at Cornell University and leads the Citizens
Speaker 1: and Technology Lab. The problems of regulating algorithms are solvable.
Speaker 1: My co host an Apple Bomb, spoke with Nathan Mathias.
Speaker 1: Here's their conversation. So Nathan, tell me what's an algorithm.
Speaker 1: Algorithms can be thought of as a recipe, a series
Speaker 1: of steps often programmed into a computer that determine how
Speaker 1: a machine behaves. But the challenge, as any cook often finds,
Speaker 1: is that when you put them out into the world,
Speaker 1: especially something of sufficient complexity, they often behave in ways
Speaker 1: that are different from what we expect. Can you just
Speaker 1: take a minute to explain how that's problematic and why
Speaker 1: why should we care that algorithms are deciding which piece
Speaker 1: of content you see on Facebook or which video you're
Speaker 1: being recommended on YouTube. Algorithms happen at all levels, from
Speaker 1: exactly how the electrons go from one point to another
Speaker 1: on the Internet to the much more high level things
Speaker 1: that we think about in our direct experience. For example,
Speaker 1: an algorithm determines what your email imbacts decides is spam,
Speaker 1: an algorithm on Twitter decides which faces to show when
Speaker 1: it's displaying a photo. And algorithms also and critically make
Speaker 1: decisions about what information to prioritize when showing us feeds
Speaker 1: on Facebook, on Twitter, when determining which adds we see
Speaker 1: which adds we don't, and those are often some of
Speaker 1: the uses of algorithms that people worry about in society
Speaker 1: and policy circles. YouTube makes a recommendation system to help
Speaker 1: us find the videos we like, and suddenly we're worrying
Speaker 1: about recommendations of extremism. Microsoft makes a fun chat assistant
Speaker 1: that will have interesting conversations with you, and now we're
Speaker 1: worrying about it learning racism and hatred. So we find
Speaker 1: that although we have the simple building blocks of an
Speaker 1: algorithm that an engineer can imagine, they often grow to
Speaker 1: be something larger than we might omit initially imagine. I've
Speaker 1: written and others have written about the problem of algorithms
Speaker 1: on Facebook favoritizing or preferring content and posts that are emotional,
Speaker 1: that are negative, that are divisive. You know, there's been
Speaker 1: an argument that that's one of the reasons why we
Speaker 1: have so much division and polarization in our societies, that
Speaker 1: we are being fed more and more excitable and angry
Speaker 1: content because the algorithm tests and guesses that that's what
Speaker 1: we're going to want to see or anyway that's what's
Speaker 1: going to keep us online or keep us using Facebook.
Speaker 1: Is it accurate? Is that how they work? We do
Speaker 1: live in a world where many of the systems that
Speaker 1: determined what we see and give our attention to our
Speaker 1: learning from our behavior, our preferences, and from the collective
Speaker 1: behavior of many others, some of who aren't paying. Some
Speaker 1: of them have motivated, coordinated campaigns to influence the algorithms,
Speaker 1: and they're adapting in real time. And so because we've
Speaker 1: never really faced a situation like this at such scale,
Speaker 1: people have a lot of concerns about how those algorithms
Speaker 1: are behaving and what they're doing to society. One of
Speaker 1: the fundamental challenges that I think scientists are still wrestling
Speaker 1: with is this challenge of influence. Typically, if a car
Speaker 1: crashes because there's say a faulty drive train, we can
Speaker 1: point to the engineering and say there's a problem with
Speaker 1: this system. With these adaptive systems, they're reacting and learning
Speaker 1: and responding to human society and human behavior. And we're
Speaker 1: still developing the scientific tools to understand what it means
Speaker 1: to have those feedback loops. And in the meantime, we
Speaker 1: have to live in a world where these things have
Speaker 1: very real power. If the Facebook algorithm is designed to
Speaker 1: keep all of us on Facebook as long as possible,
Speaker 1: who's able to watch that, who's able to control it,
Speaker 1: who's following the science? Almost no one is in a
Speaker 1: position right now to regulate and manage those algorithms. For example,
Speaker 1: in February, Facebook announced that they would be reducing the
Speaker 1: political content appearing in people's news feeds in several countries.
Speaker 1: We don't really know the details of what they're doing.
Speaker 1: We also have evidence, because they say they're doing tests,
Speaker 1: that they're not necessarily sure themselves what the impact is
Speaker 1: going to be. When you think about who currently has
Speaker 1: some power to shape what algorithms do, I think there
Speaker 1: are some people at different levels of society who have
Speaker 1: a little bit of influence. We've seen, for example, European
Speaker 1: regulators step in around antitrust around what kinds of products
Speaker 1: get promoted by search engines, for example, So governments have
Speaker 1: been doing a little bit. We definitely have companies themselves
Speaker 1: are being seen as almost government like and having policy teams,
Speaker 1: so they're trying to understand how their own systems work
Speaker 1: and manage them in some way without the rest of
Speaker 1: us having that much transparency into their values or goals
Speaker 1: or even their results. And then in some areas there
Speaker 1: are other actors who have power to manage and govern
Speaker 1: algorithms in a constraint way. So if you've ever been
Speaker 1: a Facebook group administrator, for example, or you know someone
Speaker 1: who's a Reddit moderator. They have a little bit of
Speaker 1: an ability to tweak what gets promoted or how they
Speaker 1: given algorithm works, even though they don't have a lot
Speaker 1: of visibility into the underlying code or necessarily the power
Speaker 1: to tell a big company to change what they do.
Speaker 1: A couple of weeks ago, I had reason to talk
Speaker 1: to a Facebook spokesman, and the topic was the experiments
Speaker 1: that Facebook does with its algorithms, the way in which
Speaker 1: they test different things. As you say, they try and
Speaker 1: use more or less political content. You know. Actually, after
Speaker 1: the events on January sixth at the Capitol, they came
Speaker 1: up with a way of moderating the news feed so
Speaker 1: that there wouldn't be so much disinformation in it. But
Speaker 1: of course, as you say, they don't publish the results
Speaker 1: of these experiments or of these changes. One of the
Speaker 1: solutions that I know that you have suggested is that
Speaker 1: there should be outside moderators, or there should be citizens
Speaker 1: scientists who are studying these algorithms, you know, either with
Speaker 1: the cooperation of Facebook and Google or maybe not with
Speaker 1: their cooperation. What step one The place to start is
Speaker 1: often with your own experience. I'll tell you a story
Speaker 1: just to illustrate this about six years or ago, I
Speaker 1: was approached by a group of women who faced online harassment,
Speaker 1: threats of violence, and other kinds of risks. For them.
Speaker 1: The first step was to acknowledge that it was a
Speaker 1: problem and to find other people who had the same problem.
Speaker 1: They were able to realize that they had common needs
Speaker 1: and common goals, and they actually came up with a
Speaker 1: way to record their experiences, both the kinds of harassment
Speaker 1: that they were facing, and also to record how Twitter
Speaker 1: did or often didn't handle their reports. That was the
Speaker 1: point actually that they then reached out to me and said,
Speaker 1: this is clearly a systemic problem. We've all experienced it,
Speaker 1: we want to see change. We know that better understanding
Speaker 1: data and science will help us think of better solutions
Speaker 1: and also, if necessary, to create pressure for those solutions.
Speaker 1: That was a great moment then for me and the
Speaker 1: team of researchers I led to develop a methodology and
Speaker 1: analyze the data they were collecting, and that report that
Speaker 1: we ended up creating has been influential in industry. It's
Speaker 1: helped law enforcement understand how to better support people who
Speaker 1: experience online harassment, and it's also been useful in policy
Speaker 1: debates in this country about online harassment. We're at a
Speaker 1: moment where we're still building the lines of communication and
Speaker 1: the idea of citizen science as a mode of understanding
Speaker 1: and improving our digital lives. So at this stage, I
Speaker 1: think the best first step is really to find other
Speaker 1: people who care about the thing you care about. So
Speaker 1: we need to identify the problems that have to be studied,
Speaker 1: and we need the labs where they can be studied.
Speaker 1: That's the first step. Absolutely, there's another important step at
Speaker 1: the ecosystem level. There's a funding challenge. Most of the
Speaker 1: search that goes into funding that goes into social computing
Speaker 1: comes from the tech industry, like hundreds of millions of dollars,
Speaker 1: and if you look at the money that comes into
Speaker 1: industry independent research, it's a tiny drop in the bucket.
Speaker 1: So as policymakers debate ideas like taxing tech companies, I
Speaker 1: could imagine they're being funding within that for industry independent
Speaker 1: accountability research. We're also finding ourselves having to invent new
Speaker 1: funding models for this kind of research as well. And
Speaker 1: then presumably at some point, some regulatory mechanism that makes
Speaker 1: sure that the Internet platforms will work with you and
Speaker 1: we'll listen to you exactly. So I think we're seeing
Speaker 1: more and more researchers in this space say that we're
Speaker 1: going to need some kind of regulation to provide protections
Speaker 1: and support for independent research to go on even when
Speaker 1: companies find it uncomfortable. One of the crises, you know
Speaker 1: at the moment in American life is the fact that
Speaker 1: a part of the country now lives in a completely
Speaker 1: alternative universe from the rest of the country. And we
Speaker 1: all saw on January the six that there are people
Speaker 1: who are so convinced that Donald Trump won the election
Speaker 1: that they were willing to attack the capital and even
Speaker 1: murder policemen and other in an attempt to disrupt Congress's
Speaker 1: work and prevent the naming of verifying of Joe Biden
Speaker 1: as president. How do you relate that to this problem
Speaker 1: of algorithms. I mean, if we had if we could
Speaker 1: solve the algorithm problem, if we if we were able
Speaker 1: to structure algorithms so that they favored civic discourse and
Speaker 1: civil conversation instead of promoting division and anger, could that
Speaker 1: help us heal this deep divide, this epistemological divide whereby
Speaker 1: we all live in different realities. You know, we know
Speaker 1: that when crowds of people get involved in stuff that
Speaker 1: doesn't necessarily mean that the outcome is good or better.
Speaker 1: So why should we be so sure that citizen participation
Speaker 1: in the regulation of the internet will give us good regulation.
Speaker 1: It's important to differentiate between who's making decisions and who's
Speaker 1: producing evidence. Evidence is something that you can put into
Speaker 1: the conversation about what to do, and so long as
Speaker 1: that evidence is produced in a reliable way, it has
Speaker 1: value to bring to the conversation. So your feeling is
Speaker 1: that this is a question. It's not just important for
Speaker 1: I don't know the future of social media. It's really
Speaker 1: the question it's important for democracy giving that power, giving
Speaker 1: some of that oversight ability to citizen scientists, to outside groups,
Speaker 1: maybe to some government ombudsman, maybe to some regulators, that
Speaker 1: this would democratize that power that social media companies have. Yeah,
Speaker 1: one of my personal heroes in the social sciences is
Speaker 1: Kurt Lewin, one of the founders of social psychology, who
Speaker 1: himself barely escaped Nazi Germany with his life and went
Speaker 1: on to influence so much in science and society. And
Speaker 1: he had this great quote which says, it's essential that
Speaker 1: a democratic commonwealth and its educational system apply the rational
Speaker 1: procedures of scientific investigation to its own processes of group living.
Speaker 1: And Lewin believed that that needed to be done in
Speaker 1: a democratic way if we were going to maintain the
Speaker 1: values that we have as democratic societies. That it wasn't
Speaker 1: just enough to do research that supported democracy. You needed
Speaker 1: the research itself to be democratic in nature. And I
Speaker 1: think in an era where so much of what we
Speaker 1: do is influenced by design and algorithms, that reality is
Speaker 1: clearer than it even was in Lewin's time. There's a
Speaker 1: long tradition of citizens, scientists, and outsiders working outside the
Speaker 1: government are sometimes in tandem with the government in order
Speaker 1: to push regulation or particular direction. Do you see yourself
Speaker 1: belonging to that tradition and can you describe it a
Speaker 1: little bit? You're asking me a question about something that
Speaker 1: I absolutely love and obsessed by, so question. Yeah, you know,
Speaker 1: I grew up, you know, in the United States as
Speaker 1: a Guatemalan American, with this sense that science was this
Speaker 1: tool of like powerful people in institutions that didn't always
Speaker 1: include or pay attention to the general public or the
Speaker 1: marginalized as anything other than research participants like you can
Speaker 1: be a subject in the research and we will call
Speaker 1: you a subject. But when I was a graduate at
Speaker 1: the MIT Media Lab, I started to learn about this
Speaker 1: amazing tradition of citizen science in different places and times
Speaker 1: over the last really two hundred years. In the mid
Speaker 1: nineteenth century, there was a group of people who went
Speaker 1: around London and bought bread from different shops and used
Speaker 1: this new idea of a microscope to count what was
Speaker 1: actually in the bread and found widespread food adulteration. This
Speaker 1: set of studies ended up helping launch the trajectory of
Speaker 1: what is now the Lancet, one of the premier medical
Speaker 1: journals in the world. Another example I really love is
Speaker 1: the story of the Good Housekeeping Labs, which was started
Speaker 1: just around the turn of the century. People were concerned
Speaker 1: about what was in their food, what was in other products.
Speaker 1: This was before the creation of they DA. There really
Speaker 1: wasn't that much regulation of what went into the mass
Speaker 1: production ecosystem, and so people subscribed to Good Housekeeping. Those
Speaker 1: labs would test common products and tell people if they
Speaker 1: were safe or not and use the good Housekeeping seal
Speaker 1: of approval, and often in fact in the late nineteenth
Speaker 1: early twentieth century, because there was this convergence of the
Speaker 1: rising women's movement and a passion for science, you would
Speaker 1: have women's organizations actually leading a lot of citizen science efforts.
Speaker 1: And then later on when the US established the FDA,
Speaker 1: it was actually the scientists from the Good Housekeeping Lab
Speaker 1: that built up the FDA's initial scientific capacities and leading
Speaker 1: us to where we are today, where we have more
Speaker 1: organized and supported regimes of testing and science and regulation.
Speaker 1: So when you think that's how algorithm regulation or social
Speaker 1: media regulation could evolve with teams of citizen scientists like
Speaker 1: the people at your lab, or is the idea that
Speaker 1: eventually this is something the government would do or is
Speaker 1: this something that will some other kind of civic body
Speaker 1: will do. Do you have a kind of trajectory of
Speaker 1: how this could work in the long term. In the
Speaker 1: short term, citizen science and work from the outside is
Speaker 1: a necessity. We're currently at a moment where if you
Speaker 1: want to look at what tech companies are doing from
Speaker 1: the inside, you have to sign these NDAs, you have
Speaker 1: to do work that they feel comfortable with. And like
Speaker 1: many other citizen scientists in other domains, we find ourselves
Speaker 1: inventing methodologies to answer urging questions that people need to
Speaker 1: understand now, and I think, you know, we have a
Speaker 1: small but growing number of institutions that are starting to
Speaker 1: do that work. The Barkup Consumer Reports Digital Labs has
Speaker 1: been building a team that are initiatives like Joey boil
Speaker 1: and Wine's Algorithmic Justice League that all do work of
Speaker 1: this kind. In the longer term, I would love to
Speaker 1: see a healthy ecosystem. I draw a lot of inspiration
Speaker 1: from the work of Eleanor Ostrom, the Nobel Prize winning
Speaker 1: political scientist who wrote about how you incorporate science into
Speaker 1: complex governance scenarios where you have competing interests. I think
Speaker 1: we're likely, I hope, in the long term, to get
Speaker 1: to a point where companies are going to be more transparent.
Speaker 1: They're going to actually publish their protocols and research on
Speaker 1: the issues we care about, and that's going to be
Speaker 1: an important part. I think we really desperately need more
Speaker 1: government supported efforts, and I'll leave it to the policy
Speaker 1: makers to figure out what that actually looks like. And
Speaker 1: I think will continue to see citizen scientists trying to
Speaker 1: make sense of and improve their own contexts and environment,
Speaker 1: just like we have in the arena of environmental protection,
Speaker 1: consumer protection. Those are all mature ecosystems where you have
Speaker 1: science happening from different perspectives and different points in the ecosystem. Right,
Speaker 1: So it's not just government scientists. They're also independent scientists.
Speaker 1: And there's the Sierra Club, and they're individuals and they're
Speaker 1: you know, so there there are lots of different perspectives
Speaker 1: on the same environmental problem. And you imagine that that
Speaker 1: would eventually be possible in monitoring and regulating the social
Speaker 1: media companies too, exactly, and in democracy, we hope that
Speaker 1: having multiple perspectives helps us get to a better solution.
Speaker 1: At least that's that's the vision of democracy I want.
Speaker 1: I want to cling to in how I imagine the work.
Speaker 1: And so I think we need that for governing social media,
Speaker 1: for governing the role of digital technologies in our lives,
Speaker 1: and we have a lot of work to build up
Speaker 1: the industry independent part of that ecosystem. Nathan, I know
Speaker 1: that you started your education in the humanities and you
Speaker 1: moved later on to technology. Can you tell me a
Speaker 1: little bit about how that happened? How does an English
Speaker 1: major become part of this other world? When I was
Speaker 1: a teenager, I had this amazing opportunity to meet with
Speaker 1: and talk to a local computer science professor. I was
Speaker 1: really passionate about the arts. I was really passionate about computing,
Speaker 1: and he said, computing as a lens on the world.
Speaker 1: If you really care about understanding technology, you need to
Speaker 1: understand society. You need to pay close attention to the
Speaker 1: world around you, because computing without that has no heart,
Speaker 1: it has no moral compass. With his encouragement, I felt
Speaker 1: empowered and prompted to spend my undergraduate time reading literature,
Speaker 1: studying the humanities, asking myself the big questions. Was really
Speaker 1: during my second undergraduate degree, when I was a student
Speaker 1: at Cambridge University that I started to ask questions about
Speaker 1: literature and what we read, and its impact on democracy,
Speaker 1: its impact and connections to psychology. I realized that not
Speaker 1: only were we collecting massive amounts of data about human
Speaker 1: experience and behavior that could help us answer some of
Speaker 1: those questions. I also realized that those enduring questions about
Speaker 1: what it means to live well together in society that
Speaker 1: we've been asking as long as we've had written records
Speaker 1: are incredibly important to the present time. And that's what
Speaker 1: led me to actually go back to grad school and
Speaker 1: study those questions further. And those aren't questions that are
Speaker 1: normally asked in Silicon Valley, presumably. And I don't know
Speaker 1: if I can speak for all of Silicon Valley, but
Speaker 1: I do think that I think we often hear technologists
Speaker 1: say we couldn't have known, and I can't really tell
Speaker 1: whether that's true or whether it's a rhetorical line to take,
Speaker 1: because the reality is that companies have built some of
Speaker 1: the world's most sophisticated social scientific research endeavors in the
Speaker 1: history of humanity, and the idea that they haven't turned
Speaker 1: those lenses on questions impacting the common good is just
Speaker 1: unimaginably astonishing. That it's a scandal if they haven't ask
Speaker 1: the question. It's a scandal if they've asked it and
Speaker 1: they're not telling us what they found. I want to
Speaker 1: live in a world where digital power is both guided
Speaker 1: by evidence and accountable to the public, and so I'm
Speaker 1: very dissatisfied when people tell me they haven't asked the
Speaker 1: question before. Nathan, what are a few things that you
Speaker 1: could ask our podcast listeners to do to help solve
Speaker 1: this problem themselves? So are there books you think they
Speaker 1: should read? Are there, you know things they should watch
Speaker 1: to get a better understanding these ideas or their organizations.
Speaker 1: You can suggest they be involved with things they can do. Yeah. First,
Speaker 1: there are some organizations that are building up this kind
Speaker 1: of work. You can join, subscribe, or give to organizations
Speaker 1: like the Markup, like Consumer Reports, the Algorithmic Justice League,
Speaker 1: or the Citizens and Technology Lab, which I lead. In
Speaker 1: addition to that look out for opportunities to participate in research,
Speaker 1: kat Lab will be announcing some new studies later this year.
Speaker 1: Many other researchers, some of whom I've mentioned, will announce
Speaker 1: public calls asking people sign up and help us measure
Speaker 1: or test a new idea. For example, the Mozilla Foundation,
Speaker 1: who run the Firefox browser, have a volunteer program for
Speaker 1: people to sign up and collectively monitor what kinds of
Speaker 1: recommendations YouTube is making about the role of that algorithm
Speaker 1: in our lives. That was Nathan Matthias, who leads Cornell
Speaker 1: University Citizens and Technology Lab. Will include links to his
Speaker 1: suggestions for ways that you can get involved with evaluating
Speaker 1: algorithms and improving the social media ecosystem. This is the
Speaker 1: last episode of our mini series about dealing with the
Speaker 1: problem of political polarization. I'd urge you to go back
Speaker 1: and listen to previous episodes if Eli pariser with former
Speaker 1: President Juan Manuel Santos of Columbia and of course my
Speaker 1: co host Anna Applebaum, who you've just been hearing from.
Speaker 1: When you listen to them, I think you'll come away
Speaker 1: with an understanding that polarization doesn't have to keep getting worse.
Speaker 1: It's not a one way street, and there are societies
Speaker 1: we can point to where it has gotten better. But
Speaker 1: to diminish polarization, we need to address factors propelling it
Speaker 1: in technology, media and politics. Next week I'm Solvable. We'll
Speaker 1: talk with Catherine Coleman Flowers. She's the founder and director
Speaker 1: of the Center for Rural Enterprise and Environmental Justice. We'll
Speaker 1: discuss how poor sanitation in America is solvable. Yes, it's
Speaker 1: still a problem here in the United States. I hope
Speaker 1: you'll join us. Solvable Senior producer is Jocelyn Frank. Research
Speaker 1: and booking by Lisa Dunn. Managing producer is Katherine Girardou.
Speaker 1: Mia Lobell is the executive producer of Pushkin Podcast. Solvable
Speaker 1: is a production of Pushkin Industries. If you like the show,
Speaker 1: please remember to share, rate, and review us. It really
Speaker 1: helps to get the word out. You can find Pushkin
Speaker 1: podcasts wherever you listen, including on the iHeartRadio app and
Speaker 1: Apple Podcasts. I'm Jacob Weisberg.