Speaker 1: Get in touch with technology with tech Stuff from how
Speaker 1: stuff Works dot com. Hey there, and welcome to Tech Stuff.
Speaker 1: I'm your host, Jonathan Strickland. I'm an executive producer with
Speaker 1: How Stuff Works and I love all things tech. Just
Speaker 1: a reminder, we are in our deep, dark, scary week
Speaker 1: where I'm talking about stuff that tends to have to
Speaker 1: do with a social media and the web and communications
Speaker 1: and politics. We've got midterm elections coming up pretty soon
Speaker 1: here in the United States as I record this, so
Speaker 1: these are are kind of related back to that without
Speaker 1: being too political in one stance or another. Also a reminder,
Speaker 1: I'm recovering from getting sick. I'm recording this right after
Speaker 1: I just recorded the Echo Chamber episode, and so my
Speaker 1: voice is going to continually decrease in quality over this
Speaker 1: episode and the following one, which will be Part two.
Speaker 1: So we have had tons of conversations in the United
Speaker 1: States about all things political. It's probably getting a little
Speaker 1: exhausting for those of you who are in the US.
Speaker 1: And in our last episode, I talked a little bit
Speaker 1: about propaganda and how delivering the right type of information
Speaker 1: or misinformation depending on the message to the right audience
Speaker 1: at the right time. Can really influence outcomes, and this
Speaker 1: and the next episode we're going to look at a
Speaker 1: specific story involving a company that specialized in that type
Speaker 1: of thing, or at least claimed to specialize in that
Speaker 1: type of thing. The company was the UK based Cambridge Analytica.
Speaker 1: So if you're in the United States or the UK,
Speaker 1: you've probably heard that name. If you're outside of those countries,
Speaker 1: you might have heard it because it was the center
Speaker 1: of pretty massive controversies that may have involved the personal
Speaker 1: data of up to eight seven million Facebook users, only
Speaker 1: a small number of whom agreed to share any information
Speaker 1: with this group in the first place. And you'll understand
Speaker 1: why all this means over the course of the next
Speaker 1: couple of episodes. Now, the story is a complicated one,
Speaker 1: and it involves a lot of people and other organizations,
Speaker 1: and one of the big ones we're going to talk
Speaker 1: about is Facebook and how Facebook's policies were what made
Speaker 1: Cambridge Analytica's actions possible, or at least one part of
Speaker 1: Cambridge Analytica's actions, the part that really took a lot
Speaker 1: of focus in the spring of and then there's the
Speaker 1: responsibility that we social media users bear as well. We
Speaker 1: also play a part in this. So what the heck
Speaker 1: was Cambridge Analytica because spoiler alert, the company, at least
Speaker 1: officially no longer exists. Well simply, but Cambridge Analytica was
Speaker 1: in the business of gathering information, personal information, psychological information
Speaker 1: about people and then crafting messaging specifically aimed at those
Speaker 1: people to make the biggest impact possible, specifically or political campaigns,
Speaker 1: in other words, to help elect certain candidates. Candidates that
Speaker 1: had reserved the services of Cambridge Analytica, which was acting
Speaker 1: as a political consultant. They were guns for hire. You
Speaker 1: could go out and secure the firm services and try
Speaker 1: to get elected. Um, they would gather information about the
Speaker 1: people you were hoping to reach, and they were meant
Speaker 1: to determine the most effective approach on how to do that,
Speaker 1: how to reach those people based on the information they
Speaker 1: had learned about your target audience, and they would serve
Speaker 1: up and almost personalized form of messaging based on that data,
Speaker 1: at least in theory. So it wasn't a guarantee that
Speaker 1: your message was going to be received well, but it
Speaker 1: was about as close as you can get. And in fact,
Speaker 1: sometimes you might say that Alexander Nicks, who who ran
Speaker 1: Cambridge Analytica was in fact guaranteeing those results that messaging
Speaker 1: could include misinformation, it could include propaganda, didn't have to,
Speaker 1: but it could, and it could be used to create
Speaker 1: a larger wedge between different people within a region. It
Speaker 1: was a powerful and therefore potentially dangerous claim to make. Now.
Speaker 1: According to the company itself, it would pair consumer information
Speaker 1: with psychological profile information on each person in your target audience,
Speaker 1: and it would gather that information from different sources like surveys, research,
Speaker 1: and social media platforms, namely Facebook. It would also conduct
Speaker 1: other ways to try and gather data. It would sort
Speaker 1: people into different buckets, different categories, and the four big
Speaker 1: ones would be are you confrontational or non confrontational? Are
Speaker 1: you agreeable? Are you a follower? Are you a leader?
Speaker 1: That sort of stuff, and it could formulate strategies to
Speaker 1: target each bucket based on those qualities. So if these
Speaker 1: claim as were all supported by evidence and had really
Speaker 1: strong results, it would be both really impressive and really disturbing.
Speaker 1: This idea that we can learn a lot about the
Speaker 1: people you are trying to get to support you, and
Speaker 1: we can send out the message that is most likely
Speaker 1: to get you that support disturbing because the information this
Speaker 1: company gathered about people went well beyond just general information
Speaker 1: like a name and an age and zip code or something.
Speaker 1: It would include stuff like occupations, maybe the types of
Speaker 1: shows they like to watch, the type of car they drove,
Speaker 1: their voting record, the sort of stuff they shopped for,
Speaker 1: what kind of medications they took. These were the claims
Speaker 1: Cambridge Analytica made that they had these data points. And
Speaker 1: here's the kicker, A whole lot of the information was
Speaker 1: stuff the target audience was already sharing. They were posting
Speaker 1: this information to social media, though the company was claiming
Speaker 1: to have access to these data points long before it
Speaker 1: actually got that access. So, in other words, Gambrage Analytica
Speaker 1: seemed to be making some pretty grandiose claims about their capabilities.
Speaker 1: Alexander Nicks, the head of it, was making most of
Speaker 1: those claims and very public forums before they had any
Speaker 1: real means of delivering upon it. But it did sort
Speaker 1: of catapult them into the spotlight for a while. Now,
Speaker 1: keep in mind, we are often talking in this episode
Speaker 1: and the next one about stuff that people were willingly
Speaker 1: sharing regularly on social media. It's the same sort of
Speaker 1: stuff that they might get upset about if they heard
Speaker 1: that some research firm had a spreadsheet and their name
Speaker 1: was in that spreadsheet and the data that they had
Speaker 1: just shared on Facebook or Twitter ended up being in
Speaker 1: that spreadsheet. It kind of changes the context because when
Speaker 1: you pop on Facebook, let's say you're celebrating the fact
Speaker 1: that you just bought a brand new car. To you,
Speaker 1: you are sharing some fun news with your friends. You're like, Hey,
Speaker 1: I got my new card. It's pretty cool, blah blah blah.
Speaker 1: And to you, that's just like I'm excited, be excited
Speaker 1: with me. But to these research firms, what you've done
Speaker 1: is just revealed a little bit more information about yourself
Speaker 1: that might potentially be valuable in the future, and they're
Speaker 1: taking note of it. However, it would be too easy
Speaker 1: to put all the blame on social media users. We
Speaker 1: probably could share less in a lot of cases than
Speaker 1: we are sharing, but we don't hold all the blame.
Speaker 1: Cambridge Analytica also employed tools that went beyond looking at
Speaker 1: public profiles. So this story involves not just user behavior,
Speaker 1: not just Cambridge Analyticas behavior, but also Facebook and its policies.
Speaker 1: Facebook had in place of policy that developers could take
Speaker 1: advantage of that gave those developers access to enormous amounts
Speaker 1: of data, not just the information belonging to the people
Speaker 1: who downloaded the apps, but others who never gave any
Speaker 1: consent for their information to be shared. That's also at
Speaker 1: the heart of this issue, so we're going to cover
Speaker 1: that as well. By the way, I'm making no effort
Speaker 1: to be unbiased in this episode as far as the
Speaker 1: behavior of Cambridge Analytica, because I think it's pretty clear
Speaker 1: that they did business in a dangerous and unethical way.
Speaker 1: Maybe an illegal way, but definitely in an ethical way.
Speaker 1: And it's not just my opinion. There are a lot
Speaker 1: of others who share this opinion. There's an article in
Speaker 1: mother Jones that's titled Cloak and Data, the real story
Speaker 1: behind Cambridge Analytica's Rise and Fall, which is quite good, uh.
Speaker 1: And in that article, an employee of Cambridge Analytica said
Speaker 1: that the staff would never employ their techniques on British
Speaker 1: political campaigns because it was too close to home, it
Speaker 1: was too questionable, but it seemed like it was okay
Speaker 1: to do it when it involved a different countries political system,
Speaker 1: that that would be fair game. That seems pretty ugly
Speaker 1: to me. The idea that oh, no, we would never
Speaker 1: do this here, that would be probably not cool at all,
Speaker 1: but it's okay to do it in other country's political system.
Speaker 1: That's fine. It's because that's over there, that's not over here.
Speaker 1: That's troubling to a great degree. Also spoiler alert, there
Speaker 1: are a lot of allegations that suggest that Cambridge Analytica
Speaker 1: at least had some involvement with British politics, specifically the
Speaker 1: referendum to vote on on leaving the European Union, also
Speaker 1: known as Brexit. But those are allegations that Cambridge Analytica
Speaker 1: has denied vehemently and there are ongoing investigations into that matter.
Speaker 1: So that story still hasn't played out. I'll touch on
Speaker 1: it probably again in the course of these episodes. So
Speaker 1: Cambrage Analytica was a company that, at the bottom line,
Speaker 1: would scrape data about people and then form messaging strategies
Speaker 1: around that data. So let's get into the details. So
Speaker 1: before there was at Cambridge Analytica, there was a guy
Speaker 1: named Nigel Oaks. Oaks went to Eaton, which is a
Speaker 1: boarding school in England, one of the more posh ones,
Speaker 1: certainly expensive. A lot of statesman from England have attended there. Uh,
Speaker 1: it is a boy's only boarding school, and he is
Speaker 1: an old Etonian. That's what they call the folks who
Speaker 1: who graduated from Eaton. Oaks started out in advertising. Actually
Speaker 1: he started out as a DJ, but he went on
Speaker 1: into advertising, became an executive at Sachi and Sacchi, and
Speaker 1: according to Oaks, in nine he founded an academic working
Speaker 1: group called Behavioral Dynamics Institute. And I say according to
Speaker 1: because it's really hard to track down official information about
Speaker 1: these things. The purpose of this working group was to
Speaker 1: advance research and development quote into persuasion and social influence
Speaker 1: end quote according to the official history of research group,
Speaker 1: So sort of an offshoot of advertising. How can you
Speaker 1: shape public opinion about a given subject? That was the key.
Speaker 1: How can we use our understanding of human psychology to
Speaker 1: have better success with any kind of communications campaign which
Speaker 1: could be for marketing purposes, It didn't have to be political.
Speaker 1: And the main focus was, according to Oaks, communication for
Speaker 1: conflict reduction, So an idea of how can a government
Speaker 1: or how can an organization communicate in a way that's
Speaker 1: effective to reduce conflict around the situation. Oakes brought on
Speaker 1: a couple of psychologists to work with him on this.
Speaker 1: One of them was Barry Gunter, who is now a
Speaker 1: professor emeritus at Leicester University, and the other was Adrian Furnham,
Speaker 1: and apparently the three would meet semi regularly between ninet
Speaker 1: to talk about psychology, potential applications of it in the
Speaker 1: commercial world, and about research projects. But the two psychologists
Speaker 1: eventually broke ties with Oaks. They, at least Barry Gunter,
Speaker 1: said they felt that Oaks was sort of taking their
Speaker 1: work and making grandiose promises about leveraging psychology to get results,
Speaker 1: such as in advertising, and Gunter was contacted by The
Speaker 1: New Yorker and said, quote we felt he was promising
Speaker 1: more than the science of psychology at that time could
Speaker 1: substantiate end quote. So in other words, psychologists were saying,
Speaker 1: there may be something to this, but you're making promises
Speaker 1: before we even have a full understanding, and it may
Speaker 1: even be that those promises have nothing behind them. In
Speaker 1: Behavioral Dynamics Institute had enough funding to make a go
Speaker 1: of it and became a company again according to the
Speaker 1: company's own accounts. I could not find any actual records
Speaker 1: that talked about this, but either in or in two
Speaker 1: thousand five, Behavioral Dynamics Institute created a spinoff company called
Speaker 1: Strategic Communications Laboratories or sc L, and I say either
Speaker 1: ninete or two thousand five because according to the b
Speaker 1: d I history the date is when s c L started,
Speaker 1: but the UK company's house did not register s c
Speaker 1: L as a company until two thousand five. The company
Speaker 1: official or otherwise certainly did business between nine and two
Speaker 1: thousand five. In two thousand there was an article published
Speaker 1: about the company working communications strategies for the Indonesian President Wahid, who,
Speaker 1: by the way, would later be impeached and removed from office.
Speaker 1: So I guess that messaging didn't go so well. I'll
Speaker 1: tell you more about s c L and the journey
Speaker 1: towards Cambridge Analytic in a moment, but first let's take
Speaker 1: a quick break to thank our sponsor. S c L
Speaker 1: seemed to make a lot of big promises about what
Speaker 1: it could do a psychological data After September eleven, two
Speaker 1: thousand one, the company positioned itself as an expert resource
Speaker 1: for psychological warfare and began to go after military contracts
Speaker 1: in the UK and in the US as consultants. In
Speaker 1: the early two thousands, s c L hired a salesman
Speaker 1: named Alexander Nicks, and Knicks, like Oaks, had attended Eton,
Speaker 1: so he was another old Etonian. In two thousand ten,
Speaker 1: Alexander Nicks traveled to America to scope out the possibility
Speaker 1: of finding clients for scl UH, specifically political clients. But
Speaker 1: Nick saw that the climate in the US was different
Speaker 1: than in other places. In the US, political consultants would
Speaker 1: tend to ally themselves to a particular side. You would
Speaker 1: either be Republican or Democratic consultants, but you rarely saw
Speaker 1: anyone who is sort of just a consultant for hire
Speaker 1: for either side. And s c L had clients that
Speaker 1: had leaned left and some that leaned right. So it
Speaker 1: would be really che challenging to get traction in America
Speaker 1: as a foreign company that had worked for both sides
Speaker 1: of political philosophies in the past um and so s
Speaker 1: c L did not pursue a serious effort to get
Speaker 1: into US politics at that time. By two thousand and twelve,
Speaker 1: s c L was in financial difficulty. It was not
Speaker 1: regularly pulling in consulting contracts, and there was a fundamental
Speaker 1: disagreement between Nicks, who wanted to focus more on election
Speaker 1: campaigns as a source for revenue, and Oakes, who wanted
Speaker 1: to do more work by selling operations, centers and locations
Speaker 1: across the Middle East as sort of defense contracts. So
Speaker 1: Nix and Oakes more or less part of the ways.
Speaker 1: Oakes would still head up a branch of SCL that
Speaker 1: focused on defense, and Nicks would take up a group
Speaker 1: that was called SCL Elections. So essentially they said, fine,
Speaker 1: you take our research and you apply it that way.
Speaker 1: I'm going to take our research and apply it this way.
Speaker 1: That research, by the way, from all the reports I
Speaker 1: could find, usually consisted of just just a few small,
Speaker 1: very modest studies, nothing of any real major note. Uh,
Speaker 1: so very odd that the companies were making these big,
Speaker 1: big promises because they didn't have a whole lot of
Speaker 1: research to back it up, and certainly not a lot
Speaker 1: of published research. Two thousand twelve also marked a new
Speaker 1: shot at the United States. So in in the US,
Speaker 1: the Obama campaigns had successfully leveraged Facebook and social media
Speaker 1: to reach out to young voters and to target audiences
Speaker 1: and send out messaging. Uh. Not totally different from what
Speaker 1: SCL and Cambridge Analytica would try to do, although in
Speaker 1: a way that was much more transparent. So while the
Speaker 1: Obama campaigns kind of led the way there, um, they
Speaker 1: they did not go the further the step further the
Speaker 1: Cambridge Analytica went. However, it convinced Nix that it meant
Speaker 1: that the Republican side was falling behind when it comes
Speaker 1: to technology, and there was a shift happening in the
Speaker 1: way politicians would reach out to their electorate, and because
Speaker 1: the Republican Party was falling behind, it showed an opportunity.
Speaker 1: After the two thousand twelve election, the GOP, the Grand
Speaker 1: Old Party Republican Party, issued a post mortem report that
Speaker 1: emphasized a need to look into new tools for competing
Speaker 1: in elections, including new places they had not or would
Speaker 1: not have looked before, and that gave Nix an opening.
Speaker 1: He rebranded SCL as being more than an image and
Speaker 1: communications strategy consulting firm. Now, he said, s c L
Speaker 1: is a expert in data analytics and using those results
Speaker 1: to form actionable strategies. So, according to former employees, Nix
Speaker 1: and Oakes both had a habit of promising deliverables without
Speaker 1: having a really strong plan in place on how to
Speaker 1: make good on those promises. So, in other words, they
Speaker 1: found out what the potential client wanted. They would promise
Speaker 1: to del liver that, and then they would come up
Speaker 1: for a price for that promise, and after that they
Speaker 1: would go back to their team and say, okay, I
Speaker 1: promised that we would give them X. We have to
Speaker 1: figure out how to give them X. That was kind
Speaker 1: of what the rebranding was all about. The company was
Speaker 1: positioning itself as having a deep expertise in a fairly
Speaker 1: young field. But maybe you could argue it was not
Speaker 1: quite as far along as the pitch would suggest. SCL
Speaker 1: elections were a very expensive firm to have on your campaign,
Speaker 1: and Nick's strategy was to aim for wealthy underdogs in
Speaker 1: elections around the world, people who add access to a
Speaker 1: lot of money but not many political resources. And then
Speaker 1: the next piece in the Cambridge Analytica puzzle fell into place.
Speaker 1: That piece was a data analyst and political campaign strategist
Speaker 1: named Christopher Wiley. Wiley is an anomaly someone that I
Speaker 1: really I just don't quite get. Interestingly, he had worked
Speaker 1: in political campaigns before, specifically it worked for Obama's political campaigns,
Speaker 1: and he had also acted as a consultant for the
Speaker 1: Liberal Party in Canada. Ni's met Wiley and thought that
Speaker 1: Wiley sounded like a genius and offered him a job
Speaker 1: to help build out s c LS capabilities to actually
Speaker 1: do what the company was claiming it could do. And
Speaker 1: Wiley accepted the offer, and part of the reason why,
Speaker 1: while he said he accepted it, in fact the main
Speaker 1: reason was that Nick's essentially told him you're gonna have
Speaker 1: free reign to work on your theories about data and
Speaker 1: politics and messaging. You're gonna have a a sandbox to
Speaker 1: play in where you can you can experiment all you like,
Speaker 1: and that made Widy very happy. He thought that data
Speaker 1: and politics were a place that had not yet really
Speaker 1: come to fruition. So around the same time, s c
Speaker 1: ls goal was to build out a tool it called Rippen,
Speaker 1: or maybe more accurately, its goal was to sell a
Speaker 1: tool called Rippen and then take the money from those
Speaker 1: sales and then develop the tool called Ribbon. So in
Speaker 1: other words, they were kind of selling a product they
Speaker 1: did not really have. This loops us back to all
Speaker 1: those data points the company claimed it had access to
Speaker 1: early on when I was talking about it in the
Speaker 1: opening of this episode. The Rippon tool was supposed to
Speaker 1: be software that would allow a company a campaign to
Speaker 1: manage things like a voter database, campaign efforts all in
Speaker 1: a holistic, coordinated, and micro targeted way to really have
Speaker 1: a focused strategy and actionable strategy so that at any
Speaker 1: given step you knew what was happening and what was
Speaker 1: supposed to happen next. And it sounded too good to
Speaker 1: be true, And it turned out that that was the
Speaker 1: case because Rippon which was named after Rippon, Wisconsin, that's
Speaker 1: the birthplace of the Republican Party from the nineteenth century
Speaker 1: when the the the party was founded in Rippon, Wisconsin
Speaker 1: as mostly as an effort to unify people who were
Speaker 1: um anti slavery UH and numerous other UH foundational ideas.
Speaker 1: It all started there. But this the software named after
Speaker 1: the birthplace for the Republican Party, didn't really exist as
Speaker 1: a finished product. While Nix was out there trying to
Speaker 1: sell it, it was consistently in development and the company
Speaker 1: was making it a practice to get the train running
Speaker 1: without having laid down any tracks at the beginning. So
Speaker 1: while the Rippon mess was happening, while campaigns were saying, Hey,
Speaker 1: where's this tool you've been promising us, Wiley began to
Speaker 1: look around for ways to actually make it a reality
Speaker 1: and something that would give the company an actual base
Speaker 1: to stand on, And he heard about the work of
Speaker 1: a psychologist named David Stillwell who had built a Facebook
Speaker 1: app called My Personality Now. This app would quiz users
Speaker 1: about five factors known as the Ocean model, and Ocean
Speaker 1: stands for openness, conscientiousness, extra version, agreeableness, and europe neuroticism.
Speaker 1: So it would measure you on the scale of each
Speaker 1: of those things, and then we'll tell you how your
Speaker 1: personality kind of measures up, what sort of personality you have.
Speaker 1: Psychologists had found that it was tricky to get people
Speaker 1: to agree to take those sorts of personality tests if
Speaker 1: people knew the results would be applied to stuff like
Speaker 1: politics or marketing, because they didn't want to share deeply
Speaker 1: personal reactions within those contexts. They didn't want to think, oh,
Speaker 1: I don't want to give this person information about my
Speaker 1: darkest desires or fears or whatever. But on Facebook, in
Speaker 1: the form of a quiz that promises to tell you
Speaker 1: more about yourself, that would work like Gangbusters. And I
Speaker 1: think it's pretty natural that we're all interested in stuff
Speaker 1: that addresses us, specifically that talks about us. We tend
Speaker 1: to be our own favorite topic of discussion. There's a
Speaker 1: bit of egomaniac in all of us to some extent.
Speaker 1: Now some people like me, we've got egomania and droves.
Speaker 1: But when you encounter an app on Facebook that says
Speaker 1: take this quiz to learn more about yourself, you may
Speaker 1: feel inclined to take that quiz. Here's the trick. You're
Speaker 1: not the only one learning stuff about yourself. The researchers
Speaker 1: are learning things too, and they're collecting an enormous amount
Speaker 1: of information about Facebook users. And so if you are
Speaker 1: looking at the survey, and if the survey says, hey,
Speaker 1: you can take the survey, uh, the app is going
Speaker 1: to get access to your profile to see what kind
Speaker 1: of stuff you post. It's not gonna post for you,
Speaker 1: but they can see what you've posted publicly or whatever.
Speaker 1: And you say, sure, that's fine. Whatever it's posted publicly,
Speaker 1: you can know that. Well, that would give more data
Speaker 1: to the developer who had created the survey. So they're
Speaker 1: getting information from the survey that get know what kind
Speaker 1: of personality you have, but they also know the type
Speaker 1: of stuff you like based upon your interactions on Facebook
Speaker 1: because they have access to that information as well, and
Speaker 1: they can start to try and draw correlations between data points. So,
Speaker 1: for example, they these researchers published a paper in two
Speaker 1: thousand uh talking about their findings, and they said that,
Speaker 1: you know, we found that people who displayed high intelligence
Speaker 1: also really tended to like thunderstorms, or people who are
Speaker 1: big fans of Hello Kitty. Scored really high on openness,
Speaker 1: but they scored low on agreeableness and emotional stability. The
Speaker 1: researchers knew that information because the app wouldn't again, just
Speaker 1: gather information from the quiz. The app gave researchers a
Speaker 1: chance to look at the profiles of the users themselves,
Speaker 1: you know, so whatever those users had clicked like on
Speaker 1: and Facebook, that stuff got lumped in with all the
Speaker 1: other data. And that's how they were able to draw
Speaker 1: some conclusions about relationships. And that seemed really interesting to Wiley.
Speaker 1: I'll tell you a little bit more about that in
Speaker 1: just a second, but first let's take another quick break
Speaker 1: to thank our sponsor. In two thousand and fourteen, Wiley
Speaker 1: would reach out to Stillwell and one of the other researchers,
Speaker 1: a named guy named Michael Kazinki, to find out about
Speaker 1: using the My Personality app or something akin to it
Speaker 1: in conjunction with political campaign research. At that time, Stillwell
Speaker 1: was working in the University of Cambridge's psycho Metrics Center
Speaker 1: in the UK, and Stillwell would ultimately turn down this opportunity,
Speaker 1: but a colleague of his, a Russian American psychologist named
Speaker 1: Alexander Cogan, said, I can do that. I can make
Speaker 1: something for you. I can create an app that is
Speaker 1: replicates the research that my colleagues have done. And so
Speaker 1: Cogan was tapped to create a tool that would work
Speaker 1: in a similar way to my personality, and he got
Speaker 1: to work on his own survey tool, which he called
Speaker 1: this is Your Digital Life Now. He positioned this survey
Speaker 1: to appear to be purely for academic research, and Facebook
Speaker 1: approved this survey. So the survey it self existed on
Speaker 1: top of Amazon's mechanical Turk platform, but it had this
Speaker 1: inner operability with Facebook. It was a thing that you
Speaker 1: would log into through Facebook, and it would therefore allow
Speaker 1: the survey operator to pull data from Facebook in addition
Speaker 1: to the information generated by the survey. And it wasn't
Speaker 1: just the information about the person taking the quiz, because
Speaker 1: Cogan had included a friends permission request, meaning that if
Speaker 1: you agreed to take the survey under these terms. And
Speaker 1: by the way, this was a paid survey. People were
Speaker 1: paid money to part to debate. If you agreed to
Speaker 1: take the survey and you logged in through your Facebook account,
Speaker 1: doing so would grant permission for the developer, in this
Speaker 1: case Cogan, to not just see your Facebook information, but
Speaker 1: the information of your friends. So it included stuff like
Speaker 1: your name, your age, your location, your email address, your likes,
Speaker 1: your comments, your posts, and then all the information of
Speaker 1: your friends as well. That was the part that would
Speaker 1: ultimately get Cambridge Analytica and Facebook into a heap of
Speaker 1: trouble in the United States. So Cogan builds out the survey,
Speaker 1: uses Amazon's mechanical Turk program, and it ends with that
Speaker 1: message that asks for that Facebook permission. Something between two
Speaker 1: seventy thousand and three twenty thousand people took the survey,
Speaker 1: So you're like, all right, well, that's like a quarter
Speaker 1: of a million people, a little more than a quarter
Speaker 1: of million people. That's a lot of people. But that
Speaker 1: was not the limit of data, right, because you had
Speaker 1: that friends permission. So you get the personality results, the
Speaker 1: survey results just from the people who took the survey,
Speaker 1: but you would get all the Facebook data from not
Speaker 1: just those people, but all of their friends as well.
Speaker 1: That is where you would start to hear this this
Speaker 1: figure being thrown around that the survey gathered information from
Speaker 1: fifty million users on Facebook, and then when Mark Zuckerberg
Speaker 1: appeared before Congress in the spring of eighteen, he said,
Speaker 1: actually the high end might be as much as eighties
Speaker 1: seven million, not fifty million, and maybe even worse than that.
Speaker 1: And eight seven million people might have had their data
Speaker 1: scraped in this operation with only two seventy thousand people
Speaker 1: giving consent. That brought brought the light up policy Facebook
Speaker 1: had in place for years that a lot of people
Speaker 1: had found troubling and had opposed before that point, and
Speaker 1: that would be that a person could provide con sent
Speaker 1: on behalf of everyone on their friends list to give
Speaker 1: access to their data in addition to the user's own
Speaker 1: data to an app developer. The more friends the person has,
Speaker 1: the more data the developer would collect. Now, one complicating
Speaker 1: factor that happened around this time is that Alexander Cogan
Speaker 1: also accepted an offer from St. Petersburg University in Russia,
Speaker 1: which granted Cogan research money to be an Associate Professor
Speaker 1: Coganell has denied that any of that money was used
Speaker 1: to build on this. Uh this is your Digital Life
Speaker 1: app or the resulting analysis of the data that Facebook returned.
Speaker 1: But this does raise even more eyebrows, saying, well, there
Speaker 1: seems to be a connection to Russia with this as well.
Speaker 1: That would not be the only connection, as we'll see
Speaker 1: on June four, two thousand fourteen, according to an executed
Speaker 1: contract that Chris Wiley would later supply to reporters, Cogan,
Speaker 1: acting as head of a company called Global Science Research,
Speaker 1: would hand over the data he collected to sc L
Speaker 1: in return for money, and that would seem to violate
Speaker 1: Facebook's terms of use. In fact, it doesn't seem to.
Speaker 1: It did violate Facebook's terms of use. The terms were
Speaker 1: under the understanding that Cogan was doing academic research, and
Speaker 1: part of those terms said you are not allowed to
Speaker 1: share those results or to share that data, that raw data,
Speaker 1: with a third party. You cannot do that. We don't
Speaker 1: have permission to do that. But Cogan didn't. Uh So
Speaker 1: s c L was not part of this research, not
Speaker 1: part of an academic research anyway. They wanted to make
Speaker 1: practical use of that data, so that violates the policy.
Speaker 1: Cogan would later say he felt he was being used
Speaker 1: a scapegoat. As a scapegoat in the whole matter that
Speaker 1: Cambridge Analytica and Facebook we're both pointing to Cogan and
Speaker 1: saying he's the reason why we're in this mess. While
Speaker 1: he said that the general opinion was that if no
Speaker 1: one asked for permission, no one could be told no.
Speaker 1: And so generally speaking, even if you suspected that what
Speaker 1: you were doing was wrong, you certainly wouldn't ask anyone
Speaker 1: about it because you didn't want to be told outright
Speaker 1: it was wrong, because it would mean you wouldn't be
Speaker 1: able to do it anymore. It was better to just
Speaker 1: keep doing the wrong thing, thinking it was wrong, but
Speaker 1: not knowing for sure, because you had plausible deniability, because
Speaker 1: as long as you didn't say, hey, is what we're doing,
Speaker 1: you know cool or not, no one could say no,
Speaker 1: that's not cool. Cogan, by the way, would also later
Speaker 1: say that the problem he ran into was that he
Speaker 1: didn't read Facebook's terms of service. He didn't read the
Speaker 1: part that said he wasn't allowed to share that data,
Speaker 1: but moreover, in his own terms of service in the
Speaker 1: app he had developed it stated that the information might
Speaker 1: be used to be sold to third parties. That in
Speaker 1: the terms of service that he he submitted to Facebook,
Speaker 1: it said that the data gathered could be sold. Now
Speaker 1: that's in violation of Facebook's policies, but means that neither
Speaker 1: party read the other's terms of service. And I know,
Speaker 1: I know for a fact, or at least not I
Speaker 1: don't know for a fact. I suspect that every single
Speaker 1: one of you out there, at some point or another,
Speaker 1: has signed up for something. And there was a little box,
Speaker 1: a little checkbox that said check to show that you
Speaker 1: have read the terms of service. And I know that
Speaker 1: you checked it. And I know you didn't read the
Speaker 1: terms of service because they were forty pages long and
Speaker 1: we all got better things to do with our lives.
Speaker 1: But here's the problem. Cogan didn't read those terms of service,
Speaker 1: and he hands over the date of the Camerage Analytica.
Speaker 1: Facebook didn't read Cogan's terms of service. So Facebook didn't say, hey, no,
Speaker 1: that's totally not cool, We're not going to approve your app. Instead,
Speaker 1: it all went through and this would become an enormous
Speaker 1: problem later on. Now, for Alexander Nicks, this was a
Speaker 1: great time because now he had a massive amount of
Speaker 1: data at his company's disposal, and he's been promising for
Speaker 1: a while now that he was going to have access
Speaker 1: to a whole bunch of information and they were gonna
Speaker 1: be able to draw a lot of different conclusions about
Speaker 1: that information. They're going to get very accurate pictures of
Speaker 1: various target audiences, and they'd be able to act upon
Speaker 1: that in a way that would produce real results. And
Speaker 1: now he actually had the data that he could point
Speaker 1: to and say, Hey, we've got all this information, let's
Speaker 1: make use of it. We're gonna let's make some gravy
Speaker 1: pay us money. So that made a very effective sales
Speaker 1: pitch to potential but political clients in the United States.
Speaker 1: They could kind of back up what they had been promising,
Speaker 1: at least from a we have the data standpoint, And
Speaker 1: then by chance things clicked into place for Nix and
Speaker 1: his team. There was a political consultant named Mark Block
Speaker 1: who with an associate of his, got on a flight
Speaker 1: from Los Angeles to New York and sitting in blocks
Speaker 1: row was a subcontractor who had worked with SCL and
Speaker 1: the subcontractor on the flight talked up SCLS methods and
Speaker 1: strategies and what they were planning on doing and what
Speaker 1: they were going to do with all this data, And
Speaker 1: by the end of that flight, Block and his associate
Speaker 1: were convinced that they should really get a meeting with
Speaker 1: Alexander Nicks and talk about this. So they arranged for
Speaker 1: one and Block met with Nicks and was impressed by
Speaker 1: the sales pitch, so he went on to introduce Nick's
Speaker 1: to a woman named Rebecca Mercer. Rebecca Mercer is one
Speaker 1: of the daughters of billionaire Robert Mercer, and the Mercers
Speaker 1: are really big backers for Republican nominees in the United States.
Speaker 1: They are strong financial backers of the GOP. The Mercers
Speaker 1: have also invested millions of dollars in bright Bart News,
Speaker 1: which at that time was led by Steve Bannon. The
Speaker 1: Mercers were on the lookout for a new leader in
Speaker 1: digital strategy because this was just after the elections in
Speaker 1: two thousand twelve. Mitt Romney had lost and the Mercers
Speaker 1: felt that a large reason why Romney was not able
Speaker 1: to win the election was due to the digital strategy
Speaker 1: that the Republicans had been employing up to that point.
Speaker 1: Uh keeping in mind here that that Robert Mercer comes
Speaker 1: from a computer science background, so Steve Bannon, Rebecca Mercer,
Speaker 1: and Robert Mercer listened to Alexander Nick's pitch his company's
Speaker 1: capabilities in late two thousand thirteen, reportedly aboard the Mercer
Speaker 1: families two hundred three foot yacht just gotta be a
Speaker 1: nice place to have a meeting. And the meeting apparently
Speaker 1: went well because the Mercers and Bannon invested money into
Speaker 1: a new company. The Mercer's reportedly invested about fifteen million
Speaker 1: dollars into this new company that spun off from s
Speaker 1: c L. And this company was Cambridge Analytica. And I'm
Speaker 1: gonna be talking a lot about Cambridge Analytica and s
Speaker 1: c L in the next episode. It's almost as if
Speaker 1: you can use both names and interchangeably. But in our
Speaker 1: next episode, I'll talk more about what cambrigeen Aaltica did,
Speaker 1: how it handled all that data we talked about earlier,
Speaker 1: the fallout that happened after people found out what was
Speaker 1: going on, and more. Before I leave, I do want
Speaker 1: to mention that, according to all the research I read, well,
Speaker 1: cambrag general Ittica technically spun off from s c L.
Speaker 1: The two organizations were effectively one and the same. According
Speaker 1: to multiple sources, s c L essentially handled stuff uh
Speaker 1: in the U k and outside the States, and Cambridge
Speaker 1: Gennaltica handled pretty much everything in the United States, but
Speaker 1: they depended heavily on the same staff, the same physical resources,
Speaker 1: headquartered in the same building. So while it sounds like
Speaker 1: I'm ending this episode just at the founding of Cambridge Analytica,
Speaker 1: where you know, the whole episode just leads up to
Speaker 1: that one moment, I would argue that Cambridge Gennaltica really
Speaker 1: existed the whole time, just not in name and not
Speaker 1: with the specific focus of trying to influence political elections
Speaker 1: in the United States. So in our next episode we're
Speaker 1: gonna go into more detail about how that played out.
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