A discussion on big data from the brooklyn book festival with authors cathy to kneel and tim wu cathy to kneel and tim wu. Good morning. Good morning, everyone, welcome. Im a professor at the Nyu Stern School of business, author of the forthcoming book, the person you mean to be. Im very excited about our paneled today. This morning were going to look at how big data has become an unavoidable part of our world and what are the dangers and benefits, what does et say about us, ourer fierce, our dreams and what should be thinking about and aware of if were going to go down this path. We have a terrific panel, mathematician cathy oneill and columbia halls tim law schools tim wu are with us today. Very importantly, you want to know that i promise you these are books youre going to want to purchase [laughter] thank you. And i promise you these are books youre going to want to purchase, and if you want to do that, when you leave the buildingan and turn left, findig table eight manned by barnes ba noble, you can get copies signed by the authorities after this panel. We will have timee for questions later, so please keep those in mind until we get to the q a period. Were going to begin first with cathy oneil, mathematician, former allege fund client hedge fund client, author Bloomberg View columnist. She wrote weapons of mass destruction how big data increases inequality and threatens democracy. Cathy, well begin with you. Tell us about your book. Great. Thanks, dolly, and thanks, everyone, for coming. This is super exciting. My favorite part is talking with people about the questions that come up around big data, and theres been a doozy of a week or two weeks in the last two weeks. F wow, weve got a lot to talk about. Facebook ads, algorithms, political ads. So my book is about, you know, sort of the algorithms that we need to worry about. I call the worst algorithms weapons of math destruction. And to be honest, six years ago when i decided to write this book with, i was a Data Scientist making algorithms. I was deciding who got what option on the internet. So i was deciding who would get this offer, who wouldnt get this offer, and i was doing it by the way, having been in quantum finance during the crisis, i already knew firsthand what could go wrong with algorithms. [laughter] and it burned me, right . We were working in finance, the aaa ratings and mortgagebacked securities were mathematical lies, but they were believable lices, and they took in a lot lies, and they took in a lot of people. The machine of the mortgages kept going in large part because of those aaa ratings. And it was an abuse of mathematical trust, and i didnt appreciate that. So i actually left finance hoping to do a better job. I worked in data science after joining occupy, and i soon realized that on the one hand i was doing almost exactly the same thing, so is i was instead of predicting the futures markets with my statistical algorithms, i was predicting human being actions, right . But i was also potentially doing something justti as destructives i had seen happen in finance; namely, i was choosing the winners and the losers. I realized that every Data Scientist was doing exactly that, and we were doing that based on things like do you have a mac or do you have a pc, are you using chrome . Are you using firefox . Are you a high Value Customer or a low Value Customer. And we were deciding to make better offers, essentially, to people that looked like they had more money. And that was my perspective. I wanted to i think otherwise, right . I wanted to think, oh, im doing, im doing something not as, like, more benign than finance, but the more i thought about it, the more i realized that we were very deliberately creating exactly the same kind of, like, social structures and silos on the internet that we had been trying to escape when we first created the internet. Remember when we thought the internet was a democratizing force where everybody would have equal access tode information . Well, that wasnt what i was building. So for the second time i realized that i was becoming complicit in something that was really evil. But it was actually, in my opinion, possibly worse than what had already happened in finance. Whereas in finance everyone noticed when the financial crisis happened, in this new system where we were all pushing the lucky people up and pushing the unlucky people down, it was pretty much invisible to people being pushed or nudged in either direction. And in particular, the people unfairly not getting the opportunity they should have gotten would never know they had ever been partd of an algorith, had ever been scored by an algorithm, and theyd be invisibly pushed downward. So, in other words, failures in finance were obvious to everyone. Failureso in data science were subtle but nobody would notice, nobody would fix it. Thats not fixed finance, but at least we know what the problems are. I got down to business and it was helped along by my friend carrie in the audience today, started telling about teachers, shes got a high school around the corner. Theyre being scored by a mysterious secret scoring system value added model for teachers. They didnt know how the scores were built, but they knew if they didnt get good scores they wouldnt get tenure. I looked into this i found out that this scoring system was horrible. Some teachers got a 6 one year, 96 the next year, very inconsistent scoring system, even though these teachers werent changing the way they taught and i looked into it more, teachers are getting fired in washington d. C. , more than 200 in a single year were fired because of this system. It was an arbitrary secret system. Then i started thinking, of the commonalties, between the different terrible algorithms that i was seeing and i can tell you a lot more in the next few minutes, having to do with credit scoring and getting insurance, having to do with trying to get a job, Personality Tests. Im sure a lot of you have taken Personality Tests. Secret algorithms may keep you from getting a job, you cant complain if its wrong because you dont know how it works. Powerful and unfair, i would add. People consistently and constantly getting rejected for no reason and theres no appeals process. I would see this in criminal justice. They would use the system to decide how long a person would go to prison. It wasnt a deep look into the peoples characters. These were things that were demograph e demogra demographic, by which i mean, where did you grow up . Did you grow up in a high crime area is one of the sentencing criteria, i call them weapons of math destruction, theyre secret, powerful and harmful, destructive. Not only on an individual level secretly because people didnt know about it, didnt understand it, but they were also creating these terrible negative feedback loops on society, instead of getting rid of bad teachers with the value added model. They were getting rid of teachers who didnt want to work in that system. And the final word, i want to provoke and ask questions about any other things ive said. The final thing im saying, the larger look at it, its increasing in equality. Now, i should have started this by saying, algorithms are constantly foisted upon us and described as objective, as unbiased, as if theyre going to improve the world, right . Inherently, because theyre mathematical algorithms. Thats not a fact. Theres nothing inherently fair or objective about the algorithms. What i was seeing when you add it up at every juncture of our life, pushing us down or up, this was a the cumulative effect of this algorithmic pushing and nudging was the opposite of social mobility, right . The opposite of the american dream. And the rest of our life and were going to keep you where you were. If that means you were born in a poor minority neighborhood. Youre on the lower end of every single scale. And youre born to a prestigious neighborhood, that means youre going to be considering a good bet in every situation. So far from being the objective marketing cools that we think of them as, algorithms have the potential, the bad ones have the potential to do real harm to our society and ill finish by saying, im not antialgorithm, but we do have to do a lot better. Thank you so much, cathy, secret, powerful and destructive how youre describing the algorithms that rule our life. In your book you take us through, i think you describe its a the journey of virtual lies, every domain and my sentence in reading this book, there was no where to run. Every chapter unveiled another algorithm in some way was affecting my life directly. When we get to the q a well want to hear more about that. I realize that the books are sitting back there. We are live on cspan, with permission im going out of frame to get the books so you guys can see them and introduce th them. All right. [laughter] youre going to love hearing about both of them. And mine . You want yours . Sure. Why not . A long time getting this cover. A good cover. Thank you. Oh, really, i want to tell you about tim and then were going to bring these two books together. Tim is a professor at columbia law school, contributing opinion writer for the new york times, hes best known for his work on Net Neutrality theory, he in fact coined the term Net Neutrality. Hes the author of the book the master switch, the attention merchants along with network neutrality, broadband discrimination and several other works. One of americas 100 most influential lawyers in 2013, named to the American Academy of arts and sciences in 2017. Author of the attention merchants. Tell us about this book. Sure, thanks, what a great audience, im just really pleased. I was worried it was going to be big data audience and you know, oh, from the 7th floor nobody is coming up. But the best audience. Yes, this is great. And i think this is kind of like an internet hangover panel, or hang you know what i mean . I think theres to both of our conversations, that i dont know about you, but the early 2000s, like 90s, we saw a liberating promise from tech, the web and the internet and thought all the things wed struggled with before were going to be over. Algorithms are going to solve our life problems, free stuff from google or facebook or make us better friends with people or find anything we want and he think were kind of at a point in history where people are sort of like, what happened . Like the party went sour. Like the Counter Culture in the 70s at some point, like kind of, people are picking up, what happened to the big dream . And i think, i hope that were sort of still optimistic a little about tech, but were here, i think, to deliver and sort of try to turn the ship back towards serving humanity, which would be my aspiration. So, this book is i like to write grand sweeping historical epics how i like to think of them. Its part of a trilogy. The first one was the master switch. This book, its related to big data, but the general topic of this book is the rise of human attention at an essential resource in western societies. And the rise of an industry that harvests attention and resells it. Its a story that starts here in new york city, actually in manhattan, not here, but probably brooklyn as well. With the first adsupported newspapers and runs all the way through from the conquest of the very strange Business Model, its a model where you get to know a lot about people, they didnt know much in the 19th century, but know something. Accumulate a giant audience like this one and then resell their attention to somebody else. You know, we live with it every day. Almost all the stuff we use on the computer feels like its free and thats because in fact, youre selling your data and your attention. And so i wanted to understand where that came from, how this very obscure weird business matter used to only power tabloid papers, the new york sun being the first, spread to the entire economy, and the point that i think is interesting for todays discussion is a moment in around the year 2000 when there was a startup named google that, you know, like a lot of startups had a great product, starting to gain some traction, but didnt have any Business Model. They were losing money. You know, like startups do. And you know, word was going around, this thing is great. And you know, they were like how are we going to make money. Now, it start of seems obvious in retrospect that they turned to advertising, the funny thing about google. They always had a especially larry page had this intrinsic disgust and hatred for advertising and larry page had written sort of antiadvertising manifesto for those of you who know the famous paper he wrote, describing the google algorithm and in the appendix wrote a screed against advertising, any advertisingfunded Search Engine is always going to be manipulative, always turned against the interests of users so that was, you know, googles original position. Obviously, they changed, you know . Huge piles of money have a way of changing ones mind about things, i guess. [laughter] and so they, they adopted the model. And then later on it almost seemed natural they would adopt the model. This much access to their minds, and we can in ways subtle and not subtle kind of make them to what we want or at least shape what we want. Going down that path, i think, was the path of darkness. And, you know, i understand publishers, theres a lot of good reasons for advertising, but i think the extent to which the web has become dependent on advertising has done,s has resulted in it reaching over the last year or two what i would consider a rock bottom where, you know, the web used to be kind of exciting, full of stuff. Today its become a vast wasteland with a couple of exceptions. I mean, some of that stuffs fun, but overall the sort of original promise has gotten lost, and irr think a lot of tht has to do with the nappedz of this Business Model demands of this Business Model to deliver up the page views, deliver up the clicks. Its become a giant manipulation machine, thats its only Business Model. People in theti 50s and 60s started to say, oh, man, the demand for ratings has ruined television. But i would say the contest for clicks makes the contest for ratings look dignified in comparison. [laughter] so im here to say, you know, i am at some level an optimist, i dont really sound like one right now. [laughter] but iso am at some level. There are these moments where media reset themselves. Television got better, you know, when i went mainly to a paid model or faced new competition from the web. I think we can rebuild the web, i think we can do b i think we need the sort of go back, look at the sites that have preserved whats good about them. Wikipedias a good example. Wikipedia somehow has managed, you know, its not perfect. Occasionally therell be entries on comic book characters which are longer than former president s or [laughter] you know, things like that. But, you know, system ground rules have really helped wikipedia, and ill add a political side to this. Youu notice wikipedia hasnt had a fake news problem. One of the upshots of this Business Model which just focuses on giving people what way about they want to see, maxg the hours you spend on facebook or so forth is feeding people exactly what they want. It powers the filter bubble Business Model which is another book actually, filter bubble. How is that Business Model, the incessant demand that you give people exactly what they want to see has pushed us very far towards the polarized politics of our current time. You know, everyone knows this, but the right hears exactly what they want to hear, the left hears exactly what they want to hear. It feeds upon itself and creates a vicious and insane kind of politics. I think the contest retention has a lot toit do underlyingingy when you understand it with some of the most successful leadersed the, including our president. Its almost like hes president buzzfeed. It doesnt matter, doesnt matter whether its good or bad, what are people watching, who wins the ratings at the end of every day. That has infected politics, this book tells you where. All right. All right, b love it. [applause] thanks. Id like to say you have around internet hangover as a way to frame this. Usually, tim and cathy, when you have a hangover, it was preceded by a good time yeah. At some point. [laughter] you know, sort of and so was there a point where we were at a party that was going well, or has this been a hangover from the beginning . I can i think so, yeah. I think the early 2000s. And you can differ. Maybe you were in finance, it was a different scene. But i think there was an extraordinary moment in early 2000, kind of chronicle it right here, where there was such a sense of possibility associated with the web, and maybe there still is to some degree. The idea that, you know, everyone would be free to be a publisher and sort of have their views out there. The birth of blogging, the first forums, the hobbyist sites, you know, where you could i mean, i have a lot of weird hobbies. Like what . [laughter] how long you got . Give us one. Well, lets say i was really into old motorcycles, vintage honda motorcycles for a while. Cool. Because i drove one when i was a little kid in asia. So you could find a site where they had every part identified. You know, it was like geek paradise. I also love this is still around i love the fact that you can watch a tv show and have a thousand people dissect exactly what does everything mean. But thats a little more recent. There was this moment, and it was going to fix democracy, it was going to fix everything that was wrong with the mass media. Oh, im so sick of just being given, like, the stupidity of everythings going to get better, and its going to be led by all of us. And that was a remarkable time. I guess there was a little bit of premature triumphalism. This book has an awful lot on the 60s where people were sort of in around 1969, 70 were planning, like, whats the new order about to become only to realize they were actually at the height of it, not at the beginning of something bigger. Okay. Sorry, im going on a little too long, but i think there s ws thisi real moment, and the grad failure was the failure to institutionalize it. Just kind of assumed, well, you know, its different, the old rules dont apply, didnt do anything to kind of bottle it. Okay. The exception of wikipedia which set up a lot of rules. And ill say also everyone at silicon valley, google, a lot of companies, a lot of wellmeaning people said, oh, theres no were great. We can take a standard corporate forprofit model, and that wont affect us at all, you know . Well st