television provider. big data look at analytics with michal kosinski . he talks about the method he developed for analyzing an individual's personality traits based on their social media activities. this event took place at the computer science exam in mountain view, cal on you. it is just over one hour -- california. it is just over one hour. . >> we are going to explore one of the most important facts about the president and future. large-scale data collection has and will continue to change politics in the developing and especially the developed world. increasingly, private firms are assembling profiles of individuals.

for example, every eligible voter in the u.s. with the death of information that would have made j edgar hoover we've with envy. the use of so-called data and the inferences made from it have a singular purpose. to craft and deliver messaging that will shape the future behavior of an individual, from buying a particular brand of toothpaste to discouraging a citizen from voting. ridesharingg one service, to voting on decisions like the u.k. brexit. these are assembled from what our guest rightfully calls are digital for print. the traces of digital lives, captured, sold, and analyzed. many of us to not even realize we are leaving these footprints behind.

what we listen to one streaming services. what we watch on cable tv. our guest tonight, dr. michal introduce us to his work. particularly, facebook likes and profile pictures. they will help us to begin to grasp how sussman's are being and could be used to shape our political and social reality. join me in welcoming michal kosinski to the stage. [applause] >> hi, david. good evening, everyone. david: thanks for helping us pure behind this curtain.

maybe we can begin by having you describe your work for us. what do you believe can be learned from our facebook likes and profile pictures. again for having me here. i am a competition scientist. i am working mostly with data, in particular big data. withad of spending time research subjects in my lap or running small experiments or maybe learning about people using surveys, i would look at the digital footprints that you so nicely introduced before that we are all leaving behind while using digital products and services. a is a great time to be computational scientist, a great time to be made. at the moment, because -- we all enormous amount of

digital footprints. we are an estimated megabytes per day per person. if you wanted to put it on paper, filling it out, 0s and stick it out, to one day worth of data, the stack of paper would be like from here to the sun four times over. one, you won't -- exactly. generating, we are all generating enormous amounts of information. now, this information of course our trail of our

behavior. our thoughts, feelings, social interactions. medications, purchases. even the things we never intended to say, like not sure if you guys realize this, if you e a message on facebook, and then you decide, it is 2:00 a.m. and i should not send it, and you abandon the message, guess what. it is still being saved and analyzed. it is not just this one platform. in most cases, data is preserved even if you think you have deleted it. take thisal is to data and learn something new human psychology or human behavior. one of the byproducts of doing this is i will produce models that take your jewel footprints and we will try to predict your future behavior. maybe your psychological traits,

politicalrsonality, views, and so on. what was shocking to me when i started working in this field, is how accurate those models are. shockingne thought -- thing. they are also very difficult to interpret. i know a computer can predict your future behavior. reveal or determine your psychological traits from your digital footprint. it is very difficult for a human scientist to understand how exactly the computer is doing it, which brings me to this black box problem. which basically means, it might be human psychologist, scientists, would be replaced one day by ai. you basicallye, have those models we don't really actually understand very well how they do it.

they are amazing at predicting your future behavior, your psychological traits, and so on. i worked with facebook likes quite a lot, not because facebook likes are the best type of digital foot print we are leaving behind. facebook likes are not so revealing. why? because liking happens in a public space. when you likes of it on facebook, you probably realize now your friends will see what you have liked. you wouldn't like anything really embarrassing or maybe something really boring, something you want to hide from your friends. but now, when you use your web browser or you search for something on google, or you go and buy something, you have much less choice. you will search for things he would never like on facebook.

you would visit websites you would never like on facebook. you wouldbuy stuff never like on facebook, like you would buy medicine that is very revealing of your health to read most of us do not like the medicine we are taking on facebook. if someone can get access to your data, your search data, records from your mobile phone, this digital footprint will be way more revealing than whatever i can do using facebook likes. whatever findings i am coming up with, they are just conservative estimates of what can be done more revealing data. you can see the entire industry, the entire industries, not just one industry -- they are moving towards building their business models on top of the data we are

producing. my favorite example is a credit card. how many of you guys have actually paid for the credit card recently? we have a few people that maybe didn't do their research online are so fancy, they pay for their credit card. most of us, including me, we do not pay for credit cards. if you are not paying for something, and thick about it -- a credit card is an amazing, magical thing that allows you to pay for stuff without carrying cash. there is a complicated network behind it. computers crunching data and someone. we are not -- and so on. we are not paying for it. why? we are the products of the record company. you can go to the website of visa or mastercard, or any other credit card operator, and you will see they see themselves not

as a financial company. he started as a financial company, helping to channel payments. aw they see themselves as consumer insights company. by observing the things you are buying and when you are buying them. on theh you are spending individual level. they can learn a lot about you, but they can also see -- extract information on a broader level. when they see recently people in san francisco started buying certain things or going to a certain restaurant. valuable very information that can be sold. if you are not paying for something, you most likely are a product. think about your web browser that you probably didn't pay for. your facebook accounts. your web search mechanism. one of the gazillions of apps

you have on your phone. think about how much data you are sharing with the others. facebooks your use of -- originally as a graduate student at cambridge, i believe. i believe facebook likes republic. anybody could see them. that make that kind of data sets available to you since it was just public on facebook? michal: yes, you are just pointing out, another reason why i'm using facebook likes. i was lucky to have a huge data set of volunteers that donated their facebook likes to me as well as political views. personality and other scores.

basically other parts of their facebook profiles. friend, he2007, my started this online personality questionnaire. take a standard personality test, and then he would receive feedback on your scores. it went viral. we had more than 6 million people that took the test. half of them generously gave us access to their facebook profiles. when you finish your test, it would ask you, if you would be willing in return for us offering you this interesting thing, if you would be willing to give us access to your facebook profile. which we would later use for scientific research. more than 6 million people took the test. around 3 million profiles, facebook profiles. at the beginning, in fact,

people like to say, when i graduated from high school, i already planned to run this research 20 years later. it was not the case. in my case, i kind of stumbled , kind of got into this research by accident. i was developing traditional personality questionnaires. they are composed of, i am always on time at work. i like poetry. i don't care about abstract ideas. i had this data set of facebook likes were basically people who don't like or -- i abstract ideas, i don't like to read. why would we even asked people these questions if we can just go to their facebook profile, look at their likes, and fill in

the questionnaire for them. i started running those simple machine learning models that would take your facebook likes and try to predict what would be her personality score. this worked really well, which actually was pretty disappointing for me. i spent so much time developing questionnaires. now a computer can do the same thing in a fraction of a second. we had other data in our data set. ok, we can predict personality. i wonder if we can predict political views. whether your parents were divorced or not. each time we asked the question, the computer with think and said, of course we can commit it is amazing. -- predict this. it is amazing. rerun the models with

independent data, thinking i must be doing something wrong to read even a computer can look at your facebook likes and predict with very high accuracy, close to perfect, whether you are gay or not. people don't like anything obviously gay on facebook. they do but it is a small fraction. it was based on the movies they watched, books they read. it looks very counterintuitive to me at the time you could do it. spents i got older, i more time running the models, it is pretty obvious. let me illustrate it for you guys, maybe let me try to offer you a short introduction to how those models work. it is actually pretty intuitive. if i told you there is an anonymous person and they like hello kitty. it is a brand, i am told.

[laughter] you would probably be able to figure out, if you know what hello kitty is, that this person is most likely female, young. an iphone user can read you could probably go from there and about her.inferences you would be very correct. 99% of people who like hello kitty are women. you don't need computer scientists to make inferences of this kind. most of your likes, purchases on amazon, locations that you visited with your phone most of the search queries you put in google are not so strongly really laying about your intimate traits. it is not mean they are not revealing at all. they are revealing.

to lady that you listen gaga 30 times yesterday. it is not only weird, it shows you something about your musical taste. it also probably -- it for sure little extent,ny your sexual orientation, political reviews. virtually any other psychological trait we would like to predict. it is just that the amount of information there is really tiny. for a human being, this is useless. with a computer algorithm, it can get this tiny bit of information and aggregated over dozens of digital footprints you -- thousandsehind of digital footprints you are anving behind to arrive at accurate prediction of what your profile is. this is the paper i published in 2013. about the excited

promises of this technology. i'm still excited about the promises. it is used to improve our lives in many ways and we don't realize how. if you don't believe me, think about netflix or spotify. facebook newsfeed. people spend two hours a day on average looking at it. because the ai made an accurate prediction about what your character is. it adjusted the message to make it engaging. there are also downsides, as i am sure we will be talking about. it was basically the piper i published in 2013. it got quite some press coverage.

like,f the coverage was this is so cool. we can predict whether somebody is a republican from their facebook likes. nice, shiny gadget can read said, no, you have to realize, there are tremendous consequences for the future of our society. so cool. is just this is as far as we go. interestingly, this is how the general public treated the results. policymakers and companies took notice. two weeks after the results were published, facebook changed privacy policies in such a way facebook likes were no longer public. 2013, likes republic for everyone to see. i didn't even have to be your friend on facebook to see everything you liked. showed paper, our work

by seeing what you like, i can also determine your sexual orientation. all those other intimate traits. i think it was a great thing facebook took notice and to preserve your privacy, switched that off. you also had u.s. governments and eu governments that took notice. they started working on changing the legislation to protect their sum of theom shortcomings of this phenomena. david: let's talk about some of the political uses of this work. i want to hear about how private firms are using big data analytics akin to your work to shift voting results in one way or another by micro-targeting messaging that is defined by its

intended persuasion rather than accuracy. is cambridgefirms analytical, part of a network of firms that were involved in both the u.k. brexit vote and trunk campaign. much of what we know about this story is due to recent investigative journalism. especially by the guardian newspaper. i thought i could provide the audience with a quick review of the story. cambridge analytical is a u.s. firm. robert mercer. until august, 2016, had steve bannon as the vice president. it is one of the most successful quantitative hedge fund managers. a major owner of breitbart news.

executiveon left his positions when he became manager of the trunk campaign. of course, he is now the chief strategist to president trump. they employ data mining as well as government records to develop a dossier on every u.s. voter, which was first used by the ted cruz campaign and later the trump campaign to mark a target -- micro-target their message campaign. has been a central consultant for this kind of thing with the various u.k. organizations that pushed for the brexit vote. appears tonalytica be the owner. time magazine reported yesterday

congressional investigators are looking at cambridge analytical in the context of their exploration of russian activities. included russian elements. short, quite a tangled web. you tell us about how your work relates to this whole thing? how we should think about the claims about the effectiveness of their work for the trunk campaign? michal: those are very good questions. there was a lot there. first of all, we don't really they were.fective interestingly, when you listen, saying howd by

amazingly efficient they were. when they realized governments were getting interested, some things they have done became not entirely legal, they suddenly changed theirs feel. now they say, it did not work at all. we are just making stuff up. which obviously means they are lying now or they were lying then. sure, can tell you for first of all, we have a lot of that we produce and acted amy showing such approaches work really well. only theee, it is not trump campaign, but all the politicians employing

messages like this in their campaigns. barack obama was the first politician to do it on a massive scale. outrage,emember any especially on the left side of the spectrum. hillary clinton as well. she not only spent three times more money than donald trump doing personalized targeting on social media, also hired away smarter people in my opinion. yes, she lost, but she didn't usingecause trump was some kind of magical methods. was caused by something else. can this winsk me the election, the answer is, yes and no. life when youf

are running a political campaign. like tv spots and writing articles, putting ads in the papers. everyone is using it, it is not giving anyone and unfair advantage. the only advantage here is barack obama was the first one to use it on a massive scale so it must have given him an unfair advantage. we as humans, we like to focus on the negative. it is great we focus on the negative. this is clearly a great psychological trait because it allows us to be successful as a species. but, let's put aside focusing on the negative and risky.

politiciansbout being able to personalize their message. their outcomes people seem not to notice. you one on one, that is what i can do. mean use algorithms to help to talk to you one-on-one about things most relevant to you. they ran those of the rhythms to try to understand your character, your interest, your dreams. make the message more interesting and relevant. all, has oneof outcome. messages became more important. say, yes we i could can. everymoney showing it on screen and be successful.

and i couldn't do much else. on a to settle down message that was a common denominator. not particularly aimed at anyone. now, i can talk to you about things that are relevant to you. which meansthings that are releo them. the political program became more important. it is not only that because this in turn has mattered for important outcomes. if i make a message more relevant to you, you become more engaged in politics. that is great for democracy when we have the voters more engaged in the messaging they are getting and we have more people engaged in politics and it is great. i believe. second of all, it also makes politicians think about, ok, what is important for you david? in the past i could say, yes we

can. now i must think what is important to you and and thinking about it would perhaps make me update my belief about what political -- my political agenda should be. and specifically when it comes to minorities. message onoadcast my tv come i focus on the majority interest. that is the logic of broadcasting. if i speak with people one-on-one, i now can develop interest naturally as a politician into what they have to say and what they are interested in. that is the first change, more engagement and more importance of the message. slogan.tional the second change, we saw in the recent election, politicians like donald trump but also bernie sanders, bringing in people into the political process that traditionally were not so interested. they were disengaged because they thought there was nothing in it for them.

you lookieve, even if at the new people that were brought into the political process, i think you could recognize it is great for democracy when people are engaged in politics. the other outcome of personalized marketing, if i can talk to you about things that are relevant to you, i do not need to show you the slogan 20 times. i can have a serious conversation with you through social media. basically what i'm saying is that i do not need to spend so much money or communicating -- on communicative, because i can communicate with you using relevant messages. another great outcome for democracy, which is decreasing the entry cost into politics. again, we have seen it in american elections with both donald trump and bernie sanders, who were not establishment candidates. they do not have much money compared with other politicians that were in the race.

they also do not have experience in running, you know, big scale presidential campaigns, so they had to basically do it on a budget. and in a new way. when they started doing, they started talking to people directly about the issues people cared about. which basically means at the end of the day we had two candidates, and you may be siding with one of them or both of them, but i believe it again it is good for democracy that you do not need a lot of money and backing from industry and establishment to be part of the political process. we have seen the same with brexit for instance. i am not a big fan of brexit myself, it seems actually that even people campaigning for brexit are not big fans of brexit itself. but, you know, some of the campaign was with the ragtag political militia that had no money, yet they were able to enter the political process.

david: i would like to challenge you a little bit on this characterization of this -- you know, saying that the micro-targeting is producing messages that are more relevant, um, you know, the relevance of these messages is there persuasive quality. it has nothing to do with their accuracy, or with their positive conservation to civic discourse, it is -- the be-all and end-all of the messaging is persuasion, so i see, what i see is the great danger in this work is actually an assault on consensus reality. so if we are all, if there is pervasive and endemic and a ceaseless micro-targeting, especially if the messages that are intended to be persuasive politically, you will end up --

a result that i fear is that people will end up living in different informational world, and will no longer have the ability to agree on what reality is. so how can you have a democracy if you cannot agree on what israel or what is not -- is real and what is not real? [applause] david: that is not against you. michal: i am glad you guys are clapping, because you know where there was a perfect consensual reality, you know where there was perfect consensus for reality, soviet russia. perfect, one tv station government approved, one pravda, the truth newspaper government approved. if you do something that other people did not know, you probably ended up in the concentration camp. so i am glad you guys clapped, because it was a bit ironic to me. i'm sorry.

let me actually -- why? because, thank you. [applause] michal: it is actually, no, i reacted too aggressively, but maybe because i was born behind the iron curtain in a country where we had perfect information bubble. you know, the bubble was exactly the same for everyone. america luckily, tried to break the information bubble by dropping printing presses into poland so the underground press could produce and spread some, some fake news, as the communist regime would call it. so yes, basically we have the smith now -- this myth now, which i believed for the longest time, everybody is telling you we are living in and go chambers and information bubbles and systems are giving us information crafted for us that

somehow is making society worse, because we now know different things about the bubbles not really overlapping. i was thinking, this is very bad because everybody says it is bad, but then i actually in the context of the findings started thinking about it. look, i strongly believe, and i did some research on that as well, that it is not bad at all. first of all, there is absolutely no evidence -- let we start differently. humans are just, we are just destined to live in an echo chamber. it is called confirmation psychology and it basically means that if you have a worldview and get new information, and it contradicts your worldview and is not compatible, you will have a tendency to reject it. you don't always reject it, but you like information that confirms your worldview a bit

more. it is not a bad trait to have. if you did not have it, you would be crazy because any new information you got, you would think -- ok, interesting. people running the world, very interesting. no, luckily we have this, like our brains like to have consistent mental structures there. is we willhe effects have a preference for information that confirms our views. that,e systems recognize so we will get more information that will confirm our views. so we are kind of leaving -- living in a good chambers. but those echo chambers today are way larger than they have ever been in the history of humankind. let me give you an extreme example. if we are born in a little town anywhere in the world, let's say america, 100 years ago. you only knew what your priest or your teacher or your

community told you. guess what? 99% of that was fake news. living in a society where there is not only other people like teachers, and your friends at work, social networks, journalists are trying to constantly give you some additional information, get you out of your bubble. and algorithms are entering the race and try to give you personalized views of the world. what happens is the bubble is expanding. in the process of the bubble expanding, they also stop perfectly overlapping. if you live in your bubble, by the way it is larger than whatever bubble a human has occupied ever before now. i live in my bubble, my growing bubble, and our bubbles grow apart, which makes me aware, he is any different information

bubble. tragedy, tragedy. if you live in soviet russia, you are never able to look at people with different information bubbles. so you never thought about it. living in a small, tiny echo chamber. which brings me to a related topic of fake news. andd not see any evidence, i encourage you to send me some if you have any, that we are somehow surrounded by more fake news today, that we are surrounded -- than we were surrounded by 20 years ago. why the opposite -- quite the opposite! [laughter] michal: i will take that. quite the opposite, the amount of information that we have, not only as a society but also each individual person in the room, is again away larger than whatever people had in the past. and one of the outcomes of us

having so much valid information, and also being able to access any information humanity has ever produced, including sensitive data, just with one click of the mouse on the internet, it would -- what happens is if you have fake news or you heard something, you heard a story, you can quickly debunk it. what happens in the present society is we have gotten so quick at debunking stories that you basically hear the phrase fake news, fake news, all the time, which creates an impression of -- wow, we are surrounded by fake news. of course we are, there is a lot of fake information around, but it is way less than ever in the history in the past. if someone has evidence to the contrary, i would love to receive it.

please send it to me. david: i think, from my perspective having those bubbles overlap is a really important thing. pivot andturn, let's talk about some of your very interesting work that is going on right now working with photographs of people's faces. um, particularly facebook profile pictures. and in recent talks you say that these neural networks, the machine algorithms you employ, can identify a man as straight or gay by pictures of his face, or identify a woman as extroverted or introverted by a picture of her faced it how is that possible -- face. how is that possible? michal: yes, in my recent work i got interested in seeing what

the algorithms can predict, if they can determine intimate traits based on a still image of their faces. when i mention it, especially when i mention it to colleagues in academia they say, oh my god. we lost you. [laughter] michal: and it is very bad. but what i think they keep forgetting is in fact humans are great at predicting intimate traits, determining intimate traits of other people based on their faces. we are so good at it that we kind of do not realize we are doing it all the time. let me give you an example. -- is an intimate traits, gender. emotions are intimate psychological traits. we can very quickly detect other people's emotions just buy a quick glimpse on their face,

even when they are trying to hide that emotion we are still able to detect it. think about race. like about genetic issues, there are certain disorders we can recognize by looking at some base face. -- someone's face. another genetic expression of genes is recognizing that somebody is the child of somebody else. when you say, you look like your father, you are actually saying i see through your face your genome. i see it is similar to the genome of this other guy, and he seems to be your father. it is like a magical skill. now, humans are good at predicting those kind of intimate traits, but we are not good at predicting other intimate traits like personality or sexual orientation, or political views. that there is no information about your political views on your face, but it also could mean that your brain has

not evolved or do not learn to extract this information from a human face. it might be that sexual orientation is as clear, clearly displayed on your face, as your gender, is just the human brain does not do well at revealing it. guess what, it seems that algorithms can basically do it pretty well. in terms of sexual orientation, more than 90% distinction between straight and gay men. predictions,ty those are comparable with a short personality questioner. how to computers do that? well, actually it is no easy answer, because it is a black box model that i would love to understand, but my brain is basically to week to do it -- weak to do it. it seems computers are great at

looking at giveaways that are on her face, our faces, then putting together hundreds of little pieces of signals into an accurate production of a given trait. much like facebook likes. is some evidence of that. one example is if you take one picture of an extroverted person, you would not know if they are extroverted or not, but if you take 100 pictures of extroverted people and eu overlay them, make an average face, suddenly a human being is able to very accurately distinct between an extrovert and introvert. the information is there, but it is too weak for a human being to detect. for a computer it is easy. david: with the networks you are using to do this classification were, they are explicitly and fundamentally like non-explanatory.

they do things, right? you train a classifier with pictures and you tell it, this, yes or no, yes or no, and then it starts to be able to identify cats, pictures or whatever. trained -- it is just a classifier, so whether it is picking up in the case of your work like on straight or gay detecting, is it subtle social and cultural information in the photography, or is it picking up, you know, something that is actually of the face? there is no way to know. right? michal: we can test it by changing morphological -- by taking a picture in photoshop in some elements like making your

nose longer or broader or whatnot, and see what the computer does, if it changes its mind about you. basically you can experiment with the computer and reveal what features of the face are predictive of being extroverted or introverted, gay or straight. at the end of the day, we could have a separate conversation just about that, what makes your face look extroverted or gay, but at the end of the day it does not matter whether those are more physiological features or whether it is something cultural or environmental that allows the prediction, because at the end of the day we see that the prediction is possible. when people say, what if i try hairdo andhanged my full the algorithm -- fool the algorithm. guess what, as soon as people chair their hair -- change their

hairdos, the computer would move on to you something else in the production. what i think is crucial and key in those studies, using a human face to predict traits is the following -- that we can talk about introducing good policies and introducing good technologies that will protect our data from being used to reveal our intimate traits. but at the end of the day, it is difficult to imagine this working out. why? first of all, it is difficult to police data. they can move across borders really quickly and can be encrypted, stolen, without a person even noticing. organizations such as nsa cannot reliably defend data, so how can we expect that we as individuals can protect our own data. which basically means in the

future there will be more data about us out there. algorithms are also getting better at turning the data into accurate predictions of our intimate traits, our future behavior,. and so on. moreover, even if you gave me full control over my data, there is still a lot i want to share. i want the discussion to be available to people after, i want my blog to be read by people, i want my twitter feed to be public, and most importantly i want to walk around without covering my face. [laughter] myhal: so basically what -- conclusion is, going forward it is going to be no privacy whatsoever. the sooner we realize that, the sooner we can start talking about how to make sure that the previous world is still habitable and safe and nice to live in.

now when people say, how can you say that we should work on technology to continue protecting our privacy, but yes let's do it, but realize it is a distraction because it makes people believe that we can have privacy in the future. this beliefe now, is completely wrong. two also stress the importance of that, we are having a conversation about how invasion of privacy, how revealing our intimate traits can be used to buysay manipulate us to products or maybe vote for political candidates. it makes me feel uneasy and so on. but we have to realize that outside of our western liberal free bubble, losing privacy can be a matter of life or death. think about countries where

homosexuality is punished by death. if a security officer there can take a smartphone, take a photo of somebody's face, and reveal their sexual orientation, this is really, really bad news for the gay men and women all around the world. now think about political views. think about other intimate traits like intelligence and so on. basically, the sooner that we make sure that we are acting, or anthink -- we basically take a something that there will be no privacy in the world, how do we change the international politics? how do we make sure that lives of minorities, religious minorities, sexual minorities, political minorities in other inntries are preserved, even the future world?

it is a question we should be talking about now and not how to change the policy to protect our privacy better, because guess what -- you already lost. we can win the battle, but we completely lost the war already. david: we can really talk a lot about that, because in preparing for this and reading about this a very position, you know, it is a pose privacy world, deal with it. i really truly wondered, is democracy possible in a post privacy world. tos that make the effort create laws that mitigate or doesnt such a condition, that make it sort of an existential question? it's how my thinking went. i do not think we can solve that

in the time available. i want to turn to some questions from the audience. apparently, lots of people had this question. can you change your digital profile by liking items you actually do not like, or by searching misleading things to sort of cover your tracks? [laughter] michal: that is a great question. great question. my answer would be, no you cannot. because maybe you can like some random things, but first of all it would make you look pretty weird. to your friends. people would not do it, right? imagine posting like 100 status updates and only one of them is real and the rest is some kind of random gibberish. [laughter] michal: nobody would do it, not to mention a computer would have no trouble figuring out which one of those 100 is an actual status update and which one is not.

so, no, you cannot do it because it not only would make all of those platforms like facebook and google in netflix completely not fun anymore, but it also would make your life not fun. imagine with your credit card, you have to buy 100 random things -- [laughter] buying instead of whatever you want to buy. it is not possible. let me give you -- even with facebook likes it is not possible but there was a test dog -- not possible. there was a test dog wants -- once, and the result was people have high intelligence and they tend to like, people who like curly fries on facebook tend to have higher intelligence. the correlation is actually very tiny, because the fact that you like curly fries does not make

you smart. but there is a tiny correlation that when you combine it with other bikes, you can reveal a person has a higher iq. there was a test dog when this fact was mentioned and the next day the popularity of curly fries on facebook went through the roof. everybody liked them on facebook. guess what, algorithms quickly discovered that. so this is why we call it machine learning, because it realized, ok, curly fries is no longer diagnostic of high intelligence. [laughter] suggest that it is like the pattern of data points rather than any particular data point that is where you have, or you can find a signal for those traits? though i loven curly fries --

youveryone in this room, know, it is nonsensical that that could have anything to do except by some baroque argument that there is some cultural signal in liking curly fries. i do not see it. does that for just it is not a particular data point, but it is patterns? michal: the power is in numbers. the more data you have about someone, the more accurate prediction would be. even when you look at things like signal theory. i didn't want to bring it up, but look at signal theory -- if you have a real signal, but you add a lot of random noise around it, you just need a slightly more of a signal and you are still going to discover what was the truth there. so unfortunately this is a great idea, but it is not going to work. [laughter] david: here is another representative question i think also many people asked.

um, you argue data analytics can be used to engage more people in politics, to which the writer of the question agrees, however could it not also be used to dissuade voters from voting? some argue certain political parties benefit from lower voter turnout. and it could be achieved through this sort of thing we were talking about. michal: good point. no question that trying to discourage people from voting is an awful democratic thing to do. but, you guys have to realize it is not the fault of social media that this is possible, it is the fault of the cultural and legal environment in this democracy, where it is ok to put negative adverts on tv, telling bad things about the other participants. [applause] [laughter] michal: so if you are concerned

with people being discouraged from voting, i think that it will be again, it will probably be impossible to come up with regulation that would prevent politicians from actually doing it somehow. because maybe they will not do it themselves, they will ask friends in other countries to leash out some bots that will do the job for them. at the end of the day, also when i am discouraging you from voting i am not saying do not go and vote, i make up a story and tell you about a pizza place -- [laughter] michal: so there are other ways, ways that are difficult to police. if you are concerned about people not going to vote, think about a smarter way of solving the problem than regulating traditional media or social media, like for instance -- and i am not an expert, but one thing that comes to mind is left

voting not only a privilege, but a duty that we have a citizens -- as citizens. [applause] david: the french example. [laughter] david: i think this is a very interesting question. of or maybe how can the sort technologies and techniques we have been discussing be used to allow individuals to understand, um, how they are being profiled? what they look like in these kinds of big data views? michal: great question. i see quite a few out there that are basically aimed at doing this. appsan go and open such and they will tell you how you could potentially be seen by advertisers or platforms.

one of such apps is hosted by my previous academic institution. it is called apply magic sauce.com and you can go there and basically the platform will take your facebook likes and tell you what predictions can be made based, what predictions, what can be determined from your facebook likes. i would argue a simpler way of checking how you are being perceived by the platforms. open facebook in the morning. the stories you see there are basically the stories that facebook a.i. believes you want to see. if you see stories about ufos, you probably according to facebook, you are a conspiracy theorist. if you see stories about hillary or another politician, again

this is probably how facebook sees your political views. david: i think you should find this to be a very encouraging question. how do i get into this field of research? [laughter] david: what skills do i need? michal: that is a great question. i love this question because i would encourage everyone, first of all to get into this field of research, and second of all to continue studying because it is fun. in the past i nearly dropped out of my high school because i was so excited about running my start up. and then i, in fact i dropped timesom my college three because i was so excited about running my start up. it was great. but then i discovered science, by accident, again to some extent. and it is just the best thing in the world. i have the best job that there is and i would gladly pay to do

it, which people do. they study for years, they go for phd courses and they basically paid to be, or to work in academia. there is another angle to it. i would strongly encourage every single person in the room, whatever you do, whether you are an artist or politician or journalist, you should try to learn programming. programming is actually fun. it is like a computer game, but you do not shoot at people, you build toys in a virtual space. but it changes your thinking. you start thinking in a way more organized way, you start thinking with procedures, with -- and so on. but, you know, i am sure you would not lose your current way of thinking but it enriches your thinking. i think it is important not only

because it is fun and there are good jobs out there. not only because a.i. is becoming way more, and computers and algorithms, are becoming way more important in society, which means we are surrounded by products and services that are run by software, so understanding the language of software would enable you to understand those entities, basically the a.i. overload of the better. but it would allow you to understand and think more like successful people think these days. think about it, among the most successful people recently there is a huge overrepresentation of nerds and geeks who program. those guys are shaping not only the products you are using, but also the societies that we live in. being able to think like them, being able to understand the more will i think in power or could empower anyone.

i am a social scientist, by the way. i am not a software engineer. i learned, i am not a software engineer, i learned writing my lame code using google. psychologist can do it , i am sure everyone can do it. david: we only have a few moments left. i did want to, i will use my prerogative as an interviewer, getting in the last question which was a very provocative thing that i saw in your work in your recent talks, this ingestion that -- suggestion that these classification algorithms could be used in the context of employment. and employment decision making. and i think it would be interesting, i believe that this is already at play in society,

so if you could just tell us about what you see as your concerns about that and what you think is hopeful about that. michal: that is a great question. thank you for bringing the topic up. i probably do not have to talk about concerns, because we are all concerned, or pretty widely known, humans focus on the negative. i want to focus on the positives for a second. you remember the stories about your first cars -- the first cars being introduced across the u.s. and europe, everybody with a flag warning that the cars were coming. so how i would open the conversation is we had examples in the past were new technologies scared people a lot. they focused on just the negatives and had a tendency to overlook the positive changes

that technology is bringing. and it is very much the same with the use of algorithms in hiring. there are plenty of shortcomings of algorithms in hiring, but you have to look at those shortcomings in context. the context is how we go about hiring people at the moment. we are doing it in an awful way. why? because we are being unfair in a racist, and sexist, and a just. -- agist. even a very bad algorithm would thans racist or sexist even a good recruiter. why? because everybody in this room is racist and sexist. if you do not think you are, you are dangerous. [laughter] michal: because you still are, but you are not taking steps to prevent yourself from damaging

others and hurting other people and being unfair to other people, because of those negative traits that all the humans possess. now when we go about hiring, how do we go about hiring people? we interview people. interviews are great, especially for the interviewer to feel so important. but there is a lot of scientific evidence, one of the best most well-established facts in industrial psychology that 0%erviews have close to predict ability of how well you are performing -- well you will perform at work. if you are going to be a spokesperson, then it is important how you, or how likable you are in an interview. but for most of the other jobs, a majority of other jobs it does not matter. moreover, what happens is when you interview people you basically, what the interview is

doing is measuring how much you like a person. inin, you like people -- if your community nobody has a tattoo and there is a young lady with a tattoo on her arm, whatever you are telling yourself, you will like her less than somebody without a tech to because -- tattoo, because it is something that is new or strange. so replacing biased humans with algorithms will bring more fair hiring and a better recruitment decisions. even in a way more important context, like letting people out of prisons or deciding on the length of the sentences, it sounds really -- it makes me feel uneasy when you are telling me that algorithms should decide on whether to put somebody in prison or not, but guess what, we are doing it already.

says still a judge that you will be released on parole, but in fact the judge is making the decision in many places in the u.s., it is already happening that the judge before they make a decision, they get a printout from an algorithm telling her how likely the person is to reoffend. and the judge follows that, because that is what people do, what the algorithm tells them. it makes me uneasy, but when you look at the effect of this policy, which is we can release two times more people from prison while keeping the reoffending rates stable, i am also hopeful because it means in the future we will have more free people out there, less people in prisons while having a safe society. or safer hopefully. it also means in the future we will have people being hired in a more fairway -- fair way man

today and way more fulfilled by their jobs. i am hopeful looking toward the future of our a.i. interactions. david: that is an interesting take. it made me think of a praise i heard -- a mirror-tocacy. i think we have come to the end of our time. thank you for the fascinating conversation. michal: thank you everyone. [applause] thank you. [applause] ♪

announcer: washington journal live every day with news and policy issues that impact you. sunday morning, an attorney that specializes in registration discusses the lot and relevance to the russian investigation. and patrick rossini democratic pollster -- talk about public opinion and key issues facing the trump administration. will discuss the latest intercontinental missile test from north korea. watch washington journal live sunday morning. join the discussion. sunday on american history tv on c-span3, we look at two u.s. presidents. at 6:00 p.m., john f. kennedy in life and photos.

>> the wonderful thing about the kennedys is they never pushed photographers away, they do not care how they were photographed, they did not care whether the tie was fixed or the code was on. -- coat was on. they knew if they made themselves accessible, they would be published. and of course it was a groundswell, no question about it. the media coverage of jfk was just the first time we had ever seen anything like it. announcer: that will be followed at 8:00 p.m. looking at ronald reagan and his relationship with we look ataul ii, as the book "the extraordinary untold story of the century." >> he sent a cable saying, i am praying for you. now reagan sent a cable that says, i am praying for you. it was a prayer society. and as for moscow, they are

worried at this point about a pope and theen the president. now they will really worry about it. announcer: american history tv come all weekend, every weekend on c-span3. the naacp held its annual convention in baltimore this week. the event included a discussion on how to improve the criminal justice system. among the speakers was author michael eric dyson. it is just over one hour. [applause] ♪ thank you. hello everyone. it is a pleasure to be with you today. i have the great privilege of introducing today's panel. and i'm going to do that quickly ca