Good morning. Good morning, everyone. Nice audience. Welcome. My name is dolly, chough. Author of the upcoming book the person you mean to be. Im very excited about our panel. This morning, well look how big data has become an unavoidable part of our world and what are the benefits and dangers of big data. What does it say about us, our fears, our dreams and what should we be thinking about aand aware of if we go down the path. We have a terrific panel. Cathy oneil and tim wu are with us today. En immateri aim excited to tele about them and i want to know, i promise you, these are books youll want to purchase and i promise you, these are books youll want to purchase if you want to do that, leaving the building turning left. The table at barnes noble, youll be able to have a signed copy by the author. If you have questions, keep in mind until we get to the q a. And were going to win with a mathematician, Data Scientist, author, Bloomberg View columnist, weapons of math destruction, how big data inequality and threatens democracy. Cathy, well begin with you, tell us about your book. Great, thanks everyone for coming, its super exciting and my favorite part is talking with people about the questions that come up around big date an and theres been a doozy of a week or the last two weeks, wow, a lot to talk about. From date an algorithms to facebook ads. My book is sort about the algorithms that we need to talk about. I call them the worst algorithms, weapons of math destruction. Six years ago when i started to write this book, i was a Data Scientist making algorithms, deciding who got what options on the internet so deciding who would get this offer, who wouldnt get this offer and i was doing it by the way, having been an in finance at the time of the crisis, i knew firsthand what could go wrong with algorithms and it burned me. We were working in financials. Tripleas, mortgagebacked securities were mathematical lies, but believable lies and took in a lot of people and a lot of people invested in the mortgagebacked securities ap the machine kept going and it was an abuse of mathematical trust and i didnt appreciate that so i left finance hoping to do a better job. And i did data science, and soon realized on one hand i was doing almost exactly the same thing. So, instead of predicting the futures markets with my statistical algorithms, im predicting human being actions, right . But i was also potentially doing something just as destructive as i had seen happen in finance, namely, i was choosing the winners and the losers. Not only was i choosing the winners and the losers, but i realize that every Data Scientist was doing just exactly that and doing that based on things, do you have an mac or a pc, are you using chrome, or fire fox . Are you a high Value Customer . Or are you a low Value Customer . And we were deciding to make better offers to people that looked like they had more money. And that was my perspective. I wanted to think otherwise, right, i wanted to think im doing something not as 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 that the internet was a democratizing force and everybody had access to equal information. That wasnt what i was building. For the second time i realized that i was becoming complicit in something that was really evil, but it was actually kind of, in my opinion, possibly worse than what finance had already what had happened in finance because in finance, everyone noticed when the financial crisis happened. In this new system, where were all pushing the lucky people up, pushing the unlucky people down, it was pretty much invisible to the people who were being pushed or nudged in either direction. In particular, the people unfairly not getting the opportunity they should have gotten would ever know that they had been part of an algorithm. Never been scored by an algorithm and invisibly pushed downward and not knowing what happened. In other words, failures in finance were evident to everyone. And failures in data science was possibly just as horrible. Nobody would fix it. Thats not to say that anybody 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 in favor of advertisers and you cant serve two masters. Thats googles original position and huge piles of money have a way of changing ones mind about things, i guess. And so, they adopted the model and then later on facebook almost seemed natural they would adopt a model and now, almost over the last ten 0 are 15 years most Companies Start with advertising. And i think thats one of the things that has created this web hangover, this effort to harvest attention and with it, the promise to advertisers that you can control and manipulate people, and part of it relies on the data algorithms. And part of it is we have this many billion people, this much access to their mind and we can in ways, subtle, not subtle, kind of make them do what we want or at least shape what we want. Going down that path, i think, was the path of darkness. I understand that publishers theres a lot of good reasons for advertising, but i think the extent to which the web had become dependent on advertising has resulted in reaching over the last year or two, what i consider is rock bottom. You know, the web used to be exciting for folks and today its a vast wasteland with a couple of exception of pt im sorry, overall the sort of original comes from a lot of that has to do with the demand of the Business Model to deliver up the page views, deliver up the clicks and push people in certain directions, its a giant manipulation machine and people of the 50s and 60s, television, the demand for ratings ruined television. But the content for clicks makes it look dignified in comparison. Im an optimist, but i dont sound like one now. And in this book, which is, as i said a had inventory of all of these things. There are moments where media roux he set themselves and television got better when it went to a paid model or paced new competition from the web. I think we can rebuild the web, i think you can do better and sort of need to go back, look at the sites that have preserved whats good about them. Wikiped wikipedia, for example, its somehow managed, not perfect, occasionally entries on comic book characters which are longer than former president s, you know, things like that, but some ground rules have really helped wikipedia and ill add a political side. You notice that wikipedia doesnt have a fake news problem. One of the upshots of giving people what they want to see, maximizing the hours on facebook. And feeding people exactly what they want, it empowers the filter bubble Business Model which is another book, actually. The filter bubble, and the incessant demand that you give people exactly what they want to see, has pushed us very far toward polarized politics of our times. The right hears exactly what they want to hear and left hears what they want to hear. And it creates a vicious and insane politics. I think the content has a lot to do unlyingly when you understand it, with some of the most successful leaders today, including our president. Its almost like hes president buzz feed. Doesnt matter if its good or bad or what are people watching. Who wins the ratings at the end of every day. That has infected politics, i think it all starts from somewhere and this book tells you where. All right, love it. Thank you. [applause]. I like to say internet hangover as a way to phrase it. Usually, tim and cathy, when you have a hangover, it was preceded by a good time at some point. And you know, sort of a and so, was there a point where we were a the a party that was going well or has this been a hang over from the beginning . I think so, yeah. I mean, i think the early 2000s and you can differ, maybe you were in finance with a different scene. I think there was an extraordinary moment in early 2000s, kind of chronicle it here, 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 hobbyists sites. I mean, i have a lot of weird hobbies. Like what . All you got. Give us more. Lets say i was into old motorcycles, vintage honda motorcycles for a while because i drove one when i was a little kid in asia. And so, you know, you could find a site where every part was identified. It was like geek paradise. And this is still around, i love the fact that you can watch a tv show and have a thousand people exact what does every screen mean. Thats more recent. Theres a moment, it was going to fix democracy and everything with the mass media. Im stick of being given the stupidity, everything is going to get better and led by all of us, and that was a remarkable time. I think there was premature triumphalism. Its sort of like the 60s. And 1960s what is the real order to come. They were at the height of it not the beginning of something bigger and i think the same im probably going on too long, but i think there was this real moment and the grand failure was the failure to institutionalize it. You kind of assumed, its tech, its different, didnt do anything to bottle it with the exception of wikipedia that set up rules. And everyone in the Silicon Valley thought google, a lot of wellmeaning people said theres no were great. We can take a standard corporate forprofit model and that wont affect us at all. You know, well still be a dogood kind of place, but well be reporting to shareholders now and then. And jumped on that, thought they could have your cake and eat it, too. And stayed too long at the party situation. Yeah. And walk the shame. J us m just had to say that. I went into finance in early 2007, right . So, believe it or not, i mean, and i didnt know anything about finance, i didnt know i was a nerd, a math nerd, not history i dont know history. I only now what i saw when i got there which was a bunch of very, very smug rich people. And i would ask them questions like, what if liquidity isnt infini infinite. Cathy, theres always liquidity, always. And then the crisis ensued. It was actually earlier inside than outside. It started in august of 2007. For the rest of the world it started a year later, but everybody was just like the stheir pants, and they were like wow. By the time i left in 2011. I spent four years, two years at a hedge fund and two years trying to double down and how that ended their failure. It wasnt a math problem, it was people in finance had been chasened. We are going to stay here as long as we can and get here as long a