[inaudible conversations] 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 as we can. And this is broadcast. Sorry about that. And then we went into data finance and it was like in new york, it wasnt Silicon Valley, it wasnt the center of the beast, but it would be date a sciency stuff and back in time to 2007 in finance, everybody was smug. Everybody was like, we are doing good because we are making money. I mean, literally, that was the kind of reasoning that was being held. If were doing good on the internet with fancy data models then we must, and making lots of money we must be good for the world. And it didnt go beyond that and in some places it hasnt gone beyond that. I think the rest of the world is waking up that that this isnt actually particularly good for us in society, but i dont think that everyone has heard that message yet and i dont know if they will. So, but both of you, i think, are trying to get a message out that theres with your book, cathy, we barely know its happening. By the time you work through these books, you have a sense, in tims case of being used, because the book is how our mind share, our attention, our gaze is being sold and resold, and im noticing in this room, this is, i think, an adfree space, tim, other than im not that hard core about it. But in your book you talk about how few ad free spaces there are in our minds and lives. Cathy, in your book we talk what we barely know what is happening is how the algorithms are shaping who is compete are for our bandwidth and whose students are being opened versus closed. So i jumped in there, one of the reasons i decided to write this book is that i was literally the only person like i mentioned the only person that was worried about this. I was seeing it, oh, my god, things are happening again, but no one cares. The reason i thought no one cared was because as a white, highly educated, Data Scientist working in new york, with the you know, good pocket money, i was never the victim. Right. I was creating a system that that made some people suffer and gave option, good options and opportunities to people. And none of the people building those systems were the victims. So. Thats interesting, i want to ask you about that. So the algorithms, the darkness and destructionism of algorithms that some people benefit and some suffer. Or is that all of us benefit in some ways and all of us suffer in some ways . Tim is talking about something more larger, more m meta and more diffuse when he talks about attention. And we can talk about the facebook algorithm. But i want to focus on the algorithms that are about power. Straightup power and like corporations who have minimum wage jobs, which a lot of people work at these companies, right . They dont want to actually interview everyone because its a lot of money so they give everyone Personality Tests and get rid of the 90 of the applicants this way which is for them, great. As long as the last 10 of people are great workers. Its possible that 40 of people were rejected, were rejected for no good reeb. This is not online. Youre applying for the job tfrments youre applying online. Its not in particular about privacy because you cannot say, no, i will not answer these questions when youre applying for a job. For that matter, no school teacher, Public School teacher can say, no, i refuse to be measured by this algorithm. Thats not allowed. So many of the algorithms i talk about, theyre know the something you can opt out of. And rich people typically dont have to go through this at all. People who are truly elite, like i am, i dont have to go take a Personality Test when i try to get a job because im going to get interviewed by a senior person at the company. You see what i mean . The data science in building these things, do not have to undergo them. The people who deploy them, make them. Like hire the Data Scientists, theyre not subject to them. So its really a power thing and we are more and more being sized up and divvied apart through corporate hour via these algorithms. I think thats the best way to be thinking about it. Is it fair to say that whats happening in an algorithm, you tell us a movie story in the book about a young man applying for a job and not getting the interviews. Hes qualified. Qualified. Hes going for jobs in a grocery store, technically overqualified for and hes not getting interviews. What i hear in that kind of algorithm and the others youre describing, that people cannot opt out of is that theres a predictive quality. Some data somewhere where theres a corps ration between x and y. And therefore, its decided in this, if x exists it will lead to y and therefore were going to pull out anybody with the predi predict predictiveness, were making judgments on individuals based on characteristics. We call them stereo types. You can call them stereotype machines. People are told that algorithms are fair because theyre following the numbers. Guess what, the numbers mean the data and its a reflection of our society and our society is racist, our society a classist, and. [applause]. Guest thank you or no thank you. When you follow the data youre propagating the status quo. I would say youre exacerbating the status quo. The truth is as a society were trying to evolve, were trying to evolve, and if we are following these blind my following these algorithms, were doing the opposite. Were saying, were trusting these things that are keeping us back. The first step is for us all to get it. Dont tell me my score was 20 out of 100 and i need to get fired. Explain to me how i was assessed. One piece of good news because i dont want to be a downer. In houston, a few months ago, teachers who got fired because of these scores. They sued and the judge agreed that their due process was violated. Its not proper to be assessed by an algorithm and fired for it. Were seeing this, just the beginning, because so many people are intimidated and they trust mathematics. By the way, mathematics is trustworthy and this isnt mathematics. As a person who builds algorithms, i make many, many subjective choices when i build an algorithm, its not math and i flow in the data, which which i said and i know about the mathematical elements of algorithm, but you have a great line in your book where you say predictive models are fundamentally moral the you push us to think through a norther northerly a moral lens. Theres no getting around the question of is this fair, its not fair. There is no way that an algorithm can be objective, as long as were talking about people and whether theyre good at their job and smart enough and qualified, thats not an objective question, its a subjective question. We have to decide what fairness looks like and instead of trusting that the past history, the past practice was good enough. We have to force the algorithm to bend to our will in that sense. Thank you, cathy. Tim, im going to ask you one more question and i know im monopolizing this. One question were coming to you. Be ready. Tim, i want to ask you about rituals. You talk in your book with a few different kinds of rituals and ill let you elaborate. Sure. This book, as i said, is a history of the attention harvest, so it goes through a lot of time and characters and things happening and one of the most important things how people spend their time and attention are rituals and you know, for example, just this very idea of many of you may have this idea you need to read the news every day. You know, that didnt always exist. Newspapers, of course, didnt exist, but even that idea i need to sort of catch up on the tuesday. That is a, in some ways an invention, a bad one in some ways, invention that well know the same. And that originated in the coffee houses in england in the 18th century. Another important rid ritual is thing called prime time. We all kind of know that word, but the idea of the entire nation, lets take the year 1953 or so, sunday night, 8 p. M. , the entire nation sits down basically to watch the ed sullivan show, all at the same time, everybody, focused on one speaker. Its extraordinary, nothing like that had happened before in Human History and it may never happen gep with the occasion of the super bowl. Now its a big event when people watch the same thing and its the entire nation and its unusual. The idea that after dinner everybody will sit there and focus attention to a couple of hours of tv. Monday, i love lucy, every day there was one. And prime time, its a strong thing and some people dont do it. There was that occupied art, i think, really has become the ritual thats built current web and structured our lives. That you need to check your email and see what happened on facebook, or twitter and go on snap or instagram and depending who you are, and so it again. And maybe youll itch to grab your phone, thats essentially attention capture. If you go to a site ones, thats it. It dives into the dark essentially of creating addictive product. To creating things that both kind of keep you hooked and somehow can deliver the promise of manipulation, i mate to use that word, but it is what it is. I think this is where our books du actually linked. One of the things that happened the last 10, 15 years, as happens. Were ignoring and avoiding advertising, and either by zipping through it or developing that weird kind of blindness where you dont see it. Your brain has a its methods of manipulating people who dont know theyre being manipulated. And you find yourself, why am i buying it . Maybe at some point it was subtly recommended to me, i dont know. I saw time experimenting with myself. Why do i start to believe things politically whats going on, whatever it is. I dont like to be conspiracy theorists, but there are talented data mines that looks at money. Dont think you can outsmart them. Thats our problem, we always think, you know, were vain creatures and im smarter than the algorithms. The advertisements, a lot of people say advertisements dont work on me, im special. Thank you, were going to open it up to questions. The mics are there. Please stand up. I hope this isnt a dumb question, but whats an algorithm . Oh, thank you, thank you, a great question. [applaus [applause]. Good question, im sorry i forgot. I only had ten minutes. Im glad im holding you back. And algorithm is something that you do, every person does in their own head, where they use prior information to predict something. The exam i like to give ap this is a great example because it shows how subjective it is. I make dinner for my family. I cook dinner for my family. The data i need on a given night are the ingredients in my kitchen. By the way, i cureate data. And i dont use everything in the kitchen, i dont use the packages of ramen noodles, and im curating my information. And we make decisions. You have dinner together and assess was that an access . The data could be what does success look like . For me its like my kids ate vegetable. My eightyearold, did he get to eat nutella and that matters. Overtime we optimize to success. The next day ive learned that it wasnt successful, too much nutella and not enough brooklyn. Over time the secret of dinner i cook depends on success. Facebook optimizes its algorithm, the news feed algorithm to keep you on facebook, to optimize its profit. What else, what would be the nutella version of that. How about you optimize it to giving us true facts, different algorithm. Because its a different definition, success. Does that make sense . We all do ago rim algorithms. Our next question is from here and the third all the way in the book. [inaudible]. Can you hear me . I can hear you. Talking about big data and collections and the way that guests can be steered politically. Tim wu in your book the attention merchants you talk about the decades old program, and the way that the different. But its gotten much more granular with big data and this just comes into everything from gerrymanderi gerrymandering, and the haters were in the news this week. Great, thank you. A great question. Sound like you already read the book, which thank you for that. And so in the eastern block during communist times, there would be a folder on every citizen of note that would have, you know, a bunch of everything and thought thats crazier than the ever. And the folders held for us are far more detailed and informative of every aspects of our lives and anything people, the former convention so software, and google notably has more information and its collected voluntarily. When i first signed up with facebook, ill tell them everything about myself. Im not sure why i did that. [laughter], but, oh, because then my friends will find me, Something Like that. Weve kind of willingly handed over a massive, at least multimillion transfer of data over the last ten years. Oddly enough, facebook has never paid us for that. If you mention data for the Business Model, the more you know, the people with a gambling problem, giving them casinos. And there are subtle things you know about people are very good first for advertisements, but hoding their attention. This is the politics. If you know someone is progressive, he this want a story every day about what a bastard trump is, and you want to hear it, and you just want it. If youre a conspiracy theorist, you want a con spirty theory every day, you want certain things and they will be delivered to you over and over again. And thats where attention and data meet. Thank you. Hes got it . Perfect, thanks. Im a biostatistician and i feel like theres a constant dialog, conflict at times, between the statisticians at times and those who are kind of doing big data and Data Scientists. And theres always this idea shall well, those who are doing algorithms and so forth are not using causal interests, like that. When you were talking about earlier, kind of the oppressive or the subjective way in which we analyze thing. I wanted to know if you could talk more. Even though you see them arguing for more in big data. You see, for example, the causal inference or hack thereof is being used for that and kind of reinforcing the discrimination in itself. And so, its a very interesting discourse to see how those are caig that big data doesnt have causal inference and theyre both starting with the subjective discrimination that one has. Apso, i wanted to know if you could maybe talk more about that. Cathy, do you want to take this . Im going to dumb this down. Experimentwise. I think this is the best peerme peerment experiment ive come up with. Fox news, imagine to fox news replaces with a machine algorithm. Youre experts in algorithms thanks to that man who asked me what an algorithm is. Any science would duuse the mos relevant and theyd have to define success, what is a person who is accessful at fox news in the past look like. What does it take. Standard answer is, someone who stayed for a long time. Promoted, somebody who has gotten lots of raises. Makes success, and then train the algorithm who find people who would be successful at fox news, and i chose fox news for a reason, we happen to know that women were systematically and africanamericans were systematically discriminated against, they were not allowed to succeed in that culture, right, just as a thought experiment. Now, imagine training that algorithm and applying it to a new pool of applicants, right . What would happen . It would systematically remove women and people, and africanamericans, because it would say those people dont look like people who were successful in the past. Does that make sense. Is that causal . Causality is a question why that would be requesting asking the question why werent they successful . Was it because they were a bad worker or the culture of fox news didnt allow for them to be successful. That question isnt asked by an algorithm unless you asked that question of an algorithm or another way of thinking causality, why are we choosing this would be success . All of things prone to implicit bias problems, maybe we should instead ask the question are you qualified to work here . If youre qualified, why arent you staying longer and saying, whats wrong with the culture that highly qualified people arent getting raises and promotions. Thats what we have to think of in causality. Were lazy, instead, were studying this and saved you tons on hr people. Hope that helps. Thank you very much. Unfortunately were told were at time and not going to get the remaining questions. One more . One more quick. First of all, i mean i short question, short answer. Thank you guys for all of this. [inaudible] hopeful hopefully [inaudible] whats going on and i have something to say and close this out. This book has to lot to do with your life. At the end of your life William James says your life is what you paid attention to, thats it, nothing else. I think you have to look at your attention as a resource that you spend and think how do i actually spend my attention and is it taking me to the kind of life i want . You know, writing my own book, i was like, i should read more books because when i read books, i should spend ours do you ever have the experience to go on the internet and write an email and four hours go by . Its like a casino, be aware were living in a casino and be hard core how youre spending it. The second thing, the people in tech in this room and young people, giving new birth to a better web, a better internet, you know, we can fix this, we can save it. I think thats an Important Mission going forward. The Business Models growing Business Models, but not naively thinking we can have the technology and still make billions of dollars. We need to make an internet and a web that actually serves humanity and thats a big deal, my two ideas. I love it. Thank you, guys. Thank you very much. [applaus [applause]. The authors will be signing books outside, signing table h. Urn it right when you leave the building. [inaudible conversations] [inaudible conversations] and that was authors kathy oneill and tim wu talking about data collection. Well be back with more live from the book festival. Coming up next, chris hayes. Here is a look the a some of the current best selling Nonfiction Book according to the green light bookstore in brooklyn. Love and modern society. All about love. Followed by against everything, a collection of essays from cultural critic. And singer songwriter patty smith remembers her early career and relationship with arthur maplethorpe, just kids. A reflection on race and america between the world and me, hunger, food and weight image by objection ann roxanne gay. And a look at how the u. S. Prison system is affecting the africanamerican community. A study on race in America White rage, a collection of essa essays, too much and not in the mood, trying to make it in new york city in making rent in bedsty. And according to the green light books in brooklyn, collection of essays by samantha irving, we are never meeting in real life, some of these authors have and will be appearing on book tv. Watch them on our website, booktv. Org. Former president bill clinton recently shared his thoughts on what he calls the most important political book of the last decade. You know what the exhibit is at the clinton president ial center right now . Extreme bugs. You know what my staff gave me for christmas last year . Two, not one, but two ant farms so i can have one on my desk at the president ial library and one in my office in harlem. Why . Because i am always telling them that the most important political book that nobody read or read for a political book written in the last ten years that would have made us all much healthier as citizens, is wilsons less than 250 pages long, he taught me that the combined weight on all the ants on earth is greater than the combined weight of all the people on earth. Thats quite a number of ants. [laughter] why am i telling you this . [laughter] because here is the conclusion of the book, save you the purchase price. The conclusion is that of all the species that have ever inhabited our earth and we know there are hundreds of thousands of them, right . Theyre sadly disspearing at the most rapid rate in 10,000 years, but he says the most successful species that ever lived, if you define it we had chances to be wiped out, but here we are, were still around. Most successful. Ants, termites, bees and people. Not the biggest, not the strongest and he says what do they have in common . These are the species that lived as the greatest coopera cooperato cooperators. They solved common problems to build solid futures. And he said that people are the greatest cooperators, but their great strength is our great curse. We have a consciousness and we know it. So, it makes us arrogant and we think were smarter than we are, so we tend to slice and dice ourselves in the end threatening our ability to escape when were facing a visiting challenge. This Climate Change thing gives me a kick. No one doubts or question, verifiable that more than 90 of the Scientists Say this is an existential threat to our planet. So now, the im not saying theyre wrong, but they might be wrong. Im just saying. We just had the coldest march in new york in 100something years. I was pleading for Global Warming to come back. [laughter] look, i get it, we can make all of these jokes, but the parents in the audience, name me one threat that you wouldnt take seriously if you thought the odds of doing this were more than 90 better than the odds of doing that . Give that child the seat in the back seat, theres 100 chance your child will survive a crash unless the car completely collapses. 5 of the people say, oh, no, thats crazy. Oh, theyre just tle throw the kid in the seat and let him roll around. Nobody would do that, thats what were doing on Climate Change, were throwing the kids in the back seat and letting them roll around. You can watch el wilsons discussion of the earth. Book tv, television for serious readers. Hillary clintons memoir on the 2016 president ial election was published this past week by simon schuster. What happened details her runup to the election and reflections on the campaign and aftermath. Several Media Outlets have posted reviews from the new york times, deal blooes, npr and secretary clintons media tour includes talks in cities, chicago, new york, boston, atlanta, philadelphia, and seattle. And cspan will be covering her first stop in washington d. C. , live this monday, september 18th at 7 p. M. Eastern. Look for it to reair on book tv the following weekend, september 23rd and 24th, exact Schedule Information will be available on our website, book tv. Org or on your channel guides. [inaudible conversations] [inaudible conversations] [inaudible conversations]
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