The time of shadow puppets



The United States of America is the oldest democracy in the modern world. One of the advantages of the antiquity of its democratic institutions is that America has seen every form of political and electoral corruption possible in such a system. Just in the short span of the Baby Boomer generation, we have seen spectacular examples, from the Cook County, IL graveyard turnout for Jack Kennedy in 1960 to the Florida hanging chads in 2000 to Cleveland's broken voting machines in 2004, to the present prevalence of hackable voting machines approved by bribed legislators throughout the land.


When it comes to power and money, men cheat, whenever they think they can get away with it. And new technologies are temptations not to be ignored.

The exploitation, or, in the present jargon, the weaponization of the public naivete' and amnesia about the lies, deceit, dirty tricks, miscounts, gerrymandering, the many, many modes of voter suppression, ID requirements, mistakes and errors in county clerks' offices, computerized blunders that are part and parcel of the constant ebb and flux of the voting franchise in America, create and amplify a delusion of competence. But, just because the media presents a daily parade of deluded authorities doesn't make it any less deluded.
But, in the history of dirty political campaigning, the use of data "harvested" from Facebook surpasses the old-fashioned targeted mailer (either snailmail or email) simply integrates a later generation information technology into the fouled election process.  But these times seem to be dominated by autocratic  masters of the universe who do the crime because they can, and never do the time.
Has the spreading consciousness of environmental peril stimulated the signature neurosis of our times, narcissism, an exaggerated sense of the individual's personal importance, America's belief it is the world hegemon, and collectively, the over-valuation of the human species?  Meanwhile, we have the shadow puppets, Donald Trump and Mark Zukerberg.
What follows is news, though, even if it is not quite Armageddon. Syria would probably be a better place to find the Armageddon Experience, if End Times is what ye seek.
-- blj



The Data That Turned the World Upside Down
How Cambridge Analytica used your Facebook data to help the Donald Trump campaign in the 2016 election.WEET
Hannes Grassegger & Mikael Krogerus
This article was originally published January 28, 2017.
Update: March 17, 2018: Facebook lawyer Paul Grewal announced in a blog post Friday that the company has suspended Strategic Communication Laboratories and its data analytics firm, Cambridge Analytica, from the platform.
According to Facebook, Aleksandr Kogan lied about deleting data that he obtained from a Facebook personality test and improperly passed it to third parties. As we and others have reported, Cambridge Analytica ultimately partnered with the Donald Trump campaign to leverage the data of millions of Facebook users to target them with advertisements and campaign material. The original investigation into Cambridge Analytica, published on Motherboard in January 2017 and in German in Das Magazin in December 2016, follows below.
On November 9 at around 8.30 AM., Michal Kosinski woke up in the Hotel Sunnehus in Zurich. The 34-year-old researcher had come to give a lecture at the Swiss Federal Institute of Technology (ETH) about the dangers of Big Data and the digital revolution. Kosinski gives regular lectures on this topic all over the world. He is a leading expert in psychometrics, a data-driven sub-branch of psychology. When he turned on the TV that morning, he saw that the bombshell had exploded: contrary to forecasts by all leading statisticians, Donald J. Trump had been elected president of the United States.
For a long time, Kosinski watched the Trump victory celebrations and the results coming in from each state. He had a hunch that the outcome of the election might have something to do with his research. Finally, he took a deep breath and turned off the TV.
On the same day, a then little-known British company based in London sent out a press release: "We are thrilled that our revolutionary approach to data-driven communication has played such an integral part in President-elect Trump's extraordinary win," Alexander James Ashburner Nix was quoted as saying. Nix is British, 41 years old, and CEO of Cambridge Analytica. He is always immaculately turned out in tailor-made suits and designer glasses, with his wavy blonde hair combed back from his forehead. His company wasn't just integral to Trump's online campaign, but to the UK's Brexit campaign as well.
Of these three players—reflective Kosinski, carefully groomed Nix and grinning Trump—one of them enabled the digital revolution, one of them executed it and one of them benefited from it.
How dangerous is big data?
Anyone who has not spent the last five years living on another planet will be familiar with the term Big Data. Big Data means, in essence, that everything we do, both on and offline, leaves digital traces. Every purchase we make with our cards, every search we type into Google, every movement we make when our mobile phone is in our pocket, every "like" is stored. Especially every "like." For a long time, it was not entirely clear what use this data could have—except, perhaps, that we might find ads for high blood pressure remedies just after we've Googled "reduce blood pressure."
On November 9, it became clear that maybe much more is possible. The company behind Trump's online campaign—the same company that had worked for Leave.EU in the very early stages of its "Brexit" campaign—was a Big Data company: Cambridge Analytica.
To understand the outcome of the election—and how political communication might work in the future—we need to begin with a strange incident at Cambridge University in 2014, at Kosinski's Psychometrics Center.
Psychometrics, sometimes also called psychographics, focuses on measuring psychological traits, such as personality. In the 1980s, two teams of psychologists developed a model that sought to assess human beings based on five personality traits, known as the "Big Five." These are: openness (how open you are to new experiences?), conscientiousness (how much of a perfectionist are you?), extroversion (how sociable are you?), agreeableness (how considerate and cooperative you are?) and neuroticism (are you easily upset?). Based on these dimensions—they are also known as OCEAN, an acronym for openness, conscientiousness, extroversion, agreeableness, neuroticism—we can make a relatively accurate assessment of the kind of person in front of us. This includes their needs and fears, and how they are likely to behave. The "Big Five" has become the standard technique of psychometrics. But for a long time, the problem with this approach was data collection, because it involved filling out a complicated, highly personal questionnaire. Then came the Internet. And Facebook. And Kosinski.
Michal Kosinski was a student in Warsaw when his life took a new direction in 2008. He was accepted by Cambridge University to do his PhD at the Psychometrics Centre, one of the oldest institutions of this kind worldwide. Kosinski joined fellow student David Stillwell (now a lecturer at Judge Business School at the University of Cambridge) about a year after Stillwell had launched a little Facebook application in the days when the platform had not yet become the behemoth it is today. Their MyPersonality app enabled users to fill out different psychometric questionnaires, including a handful of psychological questions from the Big Five personality questionnaire ("I panic easily," "I contradict others"). Based on the evaluation, users received a "personality profile"—individual Big Five values—and could opt-in to share their Facebook profile data with the researchers.
Kosinski had expected a few dozen college friends to fill in the questionnaire, but before long, hundreds, thousands, then millions of people had revealed their innermost convictions. Suddenly, the two doctoral candidates owned the largest dataset combining psychometric scores with Facebook profiles ever to be collected.
The approach that Kosinski and his colleagues developed over the next few years was actually quite simple. First, they provided test subjects with a questionnaire in the form of an online quiz. From their responses, the psychologists calculated the personal Big Five values of respondents. Kosinski's team then compared the results with all sorts of other online data from the subjects: what they "liked," shared or posted on Facebook, or what gender, age, place of residence they specified, for example. This enabled the researchers to connect the dots and make correlations.
Remarkably reliable deductions could be drawn from simple online actions. For example, men who "liked" the cosmetics brand MAC were slightly more likely to be gay; one of the best indicators for heterosexuality was "liking" Wu-Tang Clan. Followers of Lady Gaga were most probably extroverts, while those who "liked" philosophy tended to be introverts. While each piece of such information is too weak to produce a reliable prediction, when tens, hundreds, or thousands of individual data points are combined, the resulting predictions become really accurate.
Kosinski and his team tirelessly refined their models. In 2012, Kosinski proved that on the basis of an average of 68 Facebook "likes" by a user, it was possible to predict their skin color (with 95 percent accuracy), their sexual orientation (88 percent accuracy), and their affiliation to the Democratic or Republican party (85 percent). But it didn't stop there. Intelligence, religious affiliation, as well as alcohol, cigarette and drug use, could all be determined. From the data it was even possible to deduce whether someone's parents were divorced.
The strength of their modeling was illustrated by how well it could predict a subject's answers. Kosinski continued to work on the models incessantly: before long, he was able to evaluate a person better than the average work colleague, merely on the basis of ten Facebook "likes." Seventy "likes" were enough to outdo what a person's friends knew, 150 what their parents knew, and 300 "likes" what their partner knew. More "likes" could even surpass what a person thought they knew about themselves. On the day that Kosinski published these findings, he received two phone calls. The threat of a lawsuit and a job offer. Both from Facebook.
Only weeks later Facebook "likes" became private by default. Before that, the default setting was that anyone on the internet could see your "likes." But this was no obstacle to data collectors: while Kosinski always asked for the consent of Facebook users, many apps and online quizzes today require access to private data as a precondition for taking personality tests. (Anybody who wants to evaluate themselves based on their Facebook "likes" can do so on Kosinski's website, and then compare their results to those of a classic Ocean questionnaire, like that of the Cambridge Psychometrics Center.)
But it was not just about "likes" or even Facebook: Kosinski and his team could now ascribe Big Five values based purely on how many profile pictures a person has on Facebook, or how many contacts they have (a good indicator of extraversion). But we also reveal something about ourselves even when we're not online. For example, the motion sensor on our phone reveals how quickly we move and how far we travel (this correlates with emotional instability). Our smartphone, Kosinski concluded, is a vast psychological questionnaire that we are constantly filling out, both consciously and unconsciously.
Above all, however—and this is key—it also works in reverse: not only can psychological profiles be created from your data, but your data can also be used the other way round to search for specific profiles: all anxious fathers, all angry introverts, for example—or maybe even all undecided Democrats? Essentially, what Kosinski had invented was sort of a people search engine. He started to recognize the potential—but also the inherent danger—of his work.
To him, the internet had always seemed like a gift from heaven. What he really wanted was to give something back, to share. Data can be copied, so why shouldn't everyone benefit from it? It was the spirit of a whole generation, the beginning of a new era that transcended the limitations of the physical world. But what would happen, wondered Kosinski, if someone abused his people search engine to manipulate people? He began to add warnings to most of his scientific work. His approach, he warned, "could pose a threat to an individual's well-being, freedom, or even life." But no one seemed to grasp what he meant.
Around this time, in early 2014, Kosinski was approached by a young assistant professor in the psychology department called Aleksandr Kogan. He said he was inquiring on behalf of a company that was interested in Kosinski's method, and wanted to access the MyPersonality database. Kogan wasn't at liberty to reveal for what purpose; he was bound to secrecy.
At first, Kosinski and his team considered this offer, as it would mean a great deal of money for the institute, but then he hesitated. Finally, Kosinski remembers, Kogan revealed the name of the company: SCL, or Strategic Communication Laboratories. Kosinski Googled the company: "[We are] the premier election management agency," says the company's website. SCL provides marketing based on psychological modeling. One of its core focuses: Influencing elections. Influencing elections? Perturbed, Kosinski clicked through the pages. What kind of company was this? And what were these people planning?
What Kosinski did not know at the time: SCL is the parent of a group of companies. Who exactly owns SCL and its diverse branches is unclear, thanks to a convoluted corporate structure, the type seen in the UK Companies House, the Panama Papers, and the Delaware company registry. Some of the SCL offshoots have been involved in elections from Ukraine to Nigeriahelped the Nepalese monarch against the rebels, whereas others have developed methods to influence Eastern European andAfghan citizens for NATO. And, in 2013, SCL spun off a new company to participate in US elections: Cambridge Analytica.
Kosinski knew nothing about all this, but he had a bad feeling. "The whole thing started to stink," he recalls. On further investigation, he discovered that Aleksandr Kogan had secretly registered a company doing business with SCL. According to a December 2015 report in The Guardian and to internal company documents given to Das Magazin, it emerges that SCL learned about Kosinski's method from Kogan.
Kosinski came to suspect that Kogan's company might have reproduced the Facebook "Likes"-based Big Five measurement tool in order to sell it to this election-influencing firm. He immediately broke off contact with Kogan and informed the director of the institute, sparking a complicated conflict within the university. The institute was worried about its reputation. Aleksandr Kogan then moved to Singapore, married, and changed his name to Dr. Spectre. Michal Kosinski finished his PhD, got a job offer from Stanford and moved to the US.
Mr. Brexit
All was quiet for about a year. Then, in November 2015, the more radical of the two Brexit campaigns, "Leave.EU," supported by Nigel Farage, announced that it had commissioned a Big Data company to support its online campaign: Cambridge Analytica. The company's core strength: innovative political marketing—microtargeting—by measuring people's personality from their digital footprints, based on the OCEAN model.
Now Kosinski received emails asking what he had to do with it—the words Cambridge, personality, and analytics immediately made many people think of Kosinski. It was the first time he had heard of the company, which borrowed its name, it said, from its first employees, researchers from the university. Horrified, he looked at the website. Was his methodology being used on a grand scale for political purposes? 
After the Brexit result, friends and acquaintances wrote to him: Just look at what you've done. Everywhere he went, Kosinski had to explain that he had nothing to do with this company. (It remains unclear how deeply Cambridge Analytica was involved in the Brexit campaign. Cambridge Analytica would not discuss such questions.)
For a few months, things are relatively quiet. Then, on September 19, 2016, just over a month before the US elections, the guitar riffs of Creedence Clearwater Revival's "Bad Moon Rising" fill the dark-blue hall of New York's Grand Hyatt hotel. The Concordia Summit is a kind of World Economic Forum in miniature. Decision-makers from all over the world have been invited, among them Swiss President Johann Schneider-Ammann. "Please welcome to the stage Alexander Nix, chief executive officer of Cambridge Analytica," a smooth female voice announces. A slim man in a dark suit walks onto the stage. A hush falls. Many in attendance know that this is Trump's new digital strategy man. (A video of the presentation was posted on YouTube.)
A few weeks earlier, Trump had tweeted, somewhat cryptically, "Soon you'll be calling me Mr. Brexit." Political observers had indeed noticed some striking similarities between Trump's agenda and that of the right-wing Brexit movement. But few had noticed the connection with Trump's recent hiring of a marketing company named Cambridge Analytica.


Up to this point, Trump's digital campaign had consisted of more or less one person: Brad Parscale, a marketing entrepreneur and failed start-up founder who created a rudimentary website for Trump for $1,500. The 70-year-old Trump is not digitally savvy—there isn't even a computer on his office desk. Trump doesn't do emails, his personal assistant once revealed. She herself talked him into having a smartphone, from which he now tweets incessantly.


Hillary Clinton, on the other hand, relied heavily on the legacy of the first "social-media president," Barack Obama. She had the address lists of the Democratic Party, worked with cutting-edge big data analysts from 

BlueLabs and received support from Google and DreamWorks. When it was announced in June 2016 that Trump had hired Cambridge Analytica, the establishment in Washington just turned up their noses. Foreign dudes in tailor-made suits who don't understand the country and its people? Seriously?
"It is my privilege to speak to you today about the power of Big Data and psychographics in the electoral process." The logo of Cambridge Analytica— a brain composed of network nodes, like a map, appears behind Alexander Nix. "Only 18 months ago, Senator Cruz was one of the less popular candidates," explains the blonde man in a cut-glass British accent, which puts Americans on edge the same way that a standard German accent can unsettle Swiss people. "Less than 40 percent of the population had heard of him," another slide says. Cambridge Analytica had become involved in the US election campaign almost two years earlier, initially as a consultant for Republicans Ben Carson and Ted Cruz. Cruz—and later Trump—was funded primarily by the secretive US software billionaire Robert Mercer who, along with his daughter Rebekah, is reported to be the largest investor in Cambridge Analytica. 
"So how did he do this?" Up to now, explains Nix, election campaigns have been organized based on demographic concepts. "A really ridiculous idea. The idea that all women should receive the same message because of their gender—or all African Americans because of their race." What Nix meant is that while other campaigners so far have relied on demographics, Cambridge Analytica was using psychometrics.
Though this might be true, Cambridge Analytica's role within Cruz's campaign isn't undisputed. In December 2015 the Cruz team credited their rising success to psychological use of data and analytics. In Advertising Age, a political client said the embedded Cambridge staff was "like an extra wheel," but found their core product, Cambridge's voter data modeling, still "excellent." The campaign would pay the company at least $5.8 million to help identify voters in the Iowa caucuses, which Cruz won, before dropping out of the race in May.
Nix clicks to the next slide: five different faces, each face corresponding to a personality profile. It is the Big Five or OCEAN Model. "At Cambridge," he said, "we were able to form a model to predict the personality of every single adult in the United States of America." The hall is captivated. According to Nix, the success of Cambridge Analytica's marketing is based on a combination of three elements: behavioral science using the OCEAN Model, Big Data analysis, and ad targeting. Ad targeting is personalized advertising, aligned as accurately as possible to the personality of an individual consumer.
Nix candidly explains how his company does this. First, Cambridge Analytica buys personal data from a range of different sources, like land registries, automotive data, shopping data, bonus cards, club memberships, what magazines you read, what churches you attend. Nix displays the logos of globally active data brokers like Acxiom and Experian—in the US, almost all personal data is for sale. For example, if you want to know where Jewish women live, you can simply buy this information, phone numbers included. Now Cambridge Analytica aggregates this data with the electoral rolls of the Republican party and online data and calculates a Big Five personality profile. Digital footprints suddenly become real people with fears, needs, interests, and residential addresses.
The methodology looks quite similar to the one that Michal Kosinski once developed. Cambridge Analytica also uses, Nix told us, "surveys on social media" and Facebook data. And the company does exactly what Kosinski warned of: "We have profiled the personality of every adult in the United States of America—220 million people," Nix boasts.
He opens the screenshot. "This is a data dashboard that we prepared for the Cruz campaign." A digital control center appears. On the left are diagrams; on the right, a map of Iowa, where Cruz won a surprisingly large number of votes in the primary. And on the map, there are hundreds of thousands of small red and blue dots. Nix narrows down the criteria: "Republicans"—the blue dots disappear; "not yet convinced"—more dots disappear; "male", and so on. Finally, only one name remains, including age, address, interests, personality and political inclination. How does Cambridge Analytica now target this person with an appropriate political message?
Nix shows how psychographically categorized voters can be differently addressed, based on the example of gun rights, the 2nd Amendment: "For a highly neurotic and conscientious audience the threat of a burglary—and the insurance policy of a gun." An image on the left shows the hand of an intruder smashing a window. The right side shows a man and a child standing in a field at sunset, both holding guns, clearly shooting ducks: "Conversely, for a closed and agreeable audience. People who care about tradition, and habits, and family."
How to keep Clinton voters away from the ballot box
Trump's striking inconsistencies, his much-criticized fickleness, and the resulting array of contradictory messages, suddenly turned out to be his great asset: a different message for every voter. The notion that Trump acted like a perfectly opportunistic algorithm following audience reactions is something the mathematician Cathy O'Neil observed in August 2016.
"Pretty much every message that Trump put out was data-driven," Alexander Nix remembers. On the day of the third presidential debate between Trump and Clinton, Trump's team tested 175,000 different ad variations for his arguments, in order to find the right versions above all via Facebook. The messages differed for the most part only in microscopic details, in order to target the recipients in the optimal psychological way: different headings, colors, captions, with a photo or video. This fine-tuning reaches all the way down to the smallest groups, Nix explained in an interview with us. "We can address villages or apartment blocks in a targeted way. Even individuals."
In the Miami district of Little Haiti, for instance, Trump's campaign provided inhabitants with news about the failure of the Clinton Foundation following the earthquake in Haiti, in order to keep them from voting for Hillary Clinton. This was one of the goals: to keep potential Clinton voters (which include wavering left-wingers, African-Americans, and young women) away from the ballot box, to "suppress" their vote, as one senior campaign official told Bloomberg in the weeks before the election. These "dark posts"—sponsored news-feed-style ads in Facebook timelines that can only be seen by users with specific profiles—included videos aimed at African-Americans in which Hillary Clinton refers to black men as predators, for example.
Nix finishes his lecture at the Concordia Summit by stating that traditional blanket advertising is dead. "My children will certainly never, ever understand this concept of mass communication." And before leaving the stage, he announced that since Cruz had left the race, the company was helping one of the remaining presidential candidates.
Just how precisely the American population was being targeted by Trump's digital troops at that moment was not visible, because they attacked less on mainstream TV and more with personalized messages on social media or digital TV. And while the Clinton team thought it was in the lead, based on demographic projections, Bloomberg journalist Sasha Issenberg was surprised to note on a visit to San Antonio—where Trump's digital campaign was based—that a "second headquarters" was being created. The embedded Cambridge Analytica team, apparently only a dozen people, received $100,000 from Trump in July, $250,000 in August, and $5 million in September. According to Nix, the company earned over $15 million overall. (The company is incorporated in the US, where laws regarding the release of personal data are more lax than in European Union countries. Whereas European privacy laws require a person to "opt in" to a release of data, those in the US permit data to be released unless a user "opts out.")


Groundgame, an app for election canvassing that integrates voter data with "geospatial visualization technology," was used by campaigners for Trump and Brexit. Image: L2


The measures were radical: From July 2016, Trump's canvassers were provided with an app with which they could identify the political views and personality types of the inhabitants of a house. It was the same app provider used by Brexit campaigners. Trump's people only rang at the doors of houses that the app rated as receptive to his messages. The canvassers came prepared with guidelines for conversations tailored to the personality type of the resident. In turn, the canvassers fed the reactions into the app, and the new data flowed back to the dashboards of the Trump campaign.

Again, this is nothing new. The Democrats did similar things, but there is no evidence that they relied on psychometric profiling. Cambridge Analytica, however, divided the US population into 32 personality types, and focused on just 17 states. And just as Kosinski had established that men who like MAC cosmetics are slightly more likely to be gay, the company discovered that a preference for cars made in the US was a great indication of a potential Trump voter. Among other things, these findings now showed Trump which messages worked best and where. The decision to focus on Michigan and Wisconsin in the final weeks of the campaign was made on the basis of data analysis. The candidate became the instrument for implementing a big data model.
What's Next?
But to what extent did psychometric methods influence the outcome of the election? When asked, Cambridge Analytica was unwilling to provide any proof of the effectiveness of its campaign. And it is quite possible that the question is impossible to answer.
And yet there are clues: There is the fact of the surprising rise of Ted Cruz during the primaries. Also there was an increased number of voters in rural areas. There was the decline in the number of African-American early votes. The fact that Trump spent so little money may also be explained by the effectiveness of personality-based advertising. As does the fact that he invested far more in digital than TV campaigning compared to Hillary Clinton. Facebook proved to be the ultimate weapon and the best election campaigner, as Nix explained, and as comments by several core Trump campaigners demonstrate.
Many voices have claimed that the statisticians lost the election because their predictions were so off the mark. But what if statisticians in fact helped win the election—but only those who were using the new method? It is an irony of history that Trump, who often grumbled about scientific research, used a highly scientific approach in his campaign.
Another big winner is Cambridge Analytica. Its board member Steve Bannon, former executive chair of the right-wing online newspaper Breitbart News, has been appointed as Donald Trump's senior counselor and chief strategist. Whilst Cambridge Analytica is not willing to comment on alleged ongoing talks with UK Prime Minister Theresa May, Alexander Nix claims that he is building up his client base worldwide, and that he has received inquiries from Switzerland, Germany, and Australia. His company is currently touring European conferences showcasing their success in the United States. This year three core countries of the EU are facing elections with resurgent populist parties: France, Holland and Germany. The electoral successes come at an opportune time, as the company is readying for a push into commercial advertising.
Kosinski has observed all of this from his office at Stanford. Following the US election, the university is in turmoil. Kosinski is responding to developments with the sharpest weapon available to a researcher: a scientific analysis. Together with his research colleague Sandra Matz, he has conducted a series of tests, which will soon be published. The initial results are alarming: The study shows the effectiveness of personality targeting by showing that marketers can attract up to 63 percent more clicks and up to 1,400 more conversions in real-life advertising campaigns on Facebook when matching products and marketing messages to consumers' personality characteristics. They further demonstrate the scalability of personality targeting by showing that the majority of Facebook Pages promoting products or brands are affected by personality and that large numbers of consumers can be accurately targeted based on a single Facebook Page.
In a statement after the German publication of this article, a Cambridge Analytica spokesperson said, "Cambridge Analytica does not use data from Facebook. It has had no dealings with Dr. Michal Kosinski. It does not subcontract research. It does not use the same methodology. Psychographics was hardly used at all. Cambridge Analytica did not engage in efforts to discourage any Americans from casting their vote in the presidential election. Its efforts were solely directed towards increasing the number of voters in the election."


The world has been turned upside down. Great Britain is leaving the EU, Donald Trump is president of the United States of America. And in Stanford, Kosinski, who wanted to warn against the danger of using psychological targeting in a political setting, is once again receiving accusatory emails. "No," says Kosinski, quietly and shaking his head. "This is not my fault. I did not build the bomb. I only showed that it exists."


Additional research for this report was provided by 

Paul-Olivier Dehaye.
Here’s the transcript of Recode’s interview with Facebook CEO Mark Zuckerberg about the Cambridge Analytica controversy and more
“I think we let the community down, and I feel really bad and I’m sorry about that,” he said.
By Kara Swisher and Kurt Wagner  
Sharing is caring, except this time.
Facebook CEO Mark Zuckerberg gave interviews yesterday to several news organizations, including Recode, in an attempt to stem the fast-growing controversy about misuse of user data by a third-party developer, Cambridge Analytica.
In a wide-ranging interview, he admitted the social networking giant may have made mistakes in opening up its network so much a decade ago and that it led to the recent problems. Zuckerberg said that fixing those issues will now cost the company “many millions” of dollars.
As Facebook’s stock continued to get hammered because of Wall Street worries about the impact in its business, Zuckerberg also said he was “open” to testifying to Congress, even as legislators ever more loudly call for his appearance in hearings.
And that is not all Silicon Valley's most famous mogul said, which is why we are posting the transcript of the 20-minute interview, which was conducted by Kara Swisher and Kurt Wagner of Recode.
A short amount of cross-talk about setting up the taping of the interview at the start was removed, but here is the interview (with some small adjustments to explain references made).
Kara Swisher: As you know from us emailing, I’m very interested in tough substantive discussions and questions about this, so that’s why I’ve been so adamant. So let’s just get started. Talk a little bit about the things you announced today. Let’s have you explain each of them very briefly.
Mark Zuckerberg: Sure. At a high level, this is a major breach of trust issue, and our high-level responsibility is to make sure that this doesn’t happen again. So, if you look at the problem, it kind of breaks down into a couple of areas. One is making sure that going forward, developers can’t get access to more data than they should. The good news there is that actually the most important changes to the platform we made in 2014, three or four years ago, to restrict apps like [researcher Aleksandr Kogan’s] from being able to access a person’s “friends” data in addition to theirs.
So that was the most important thing, but then what we did on our platform is we also are closing down a number of other policies. Like, for example, if you haven’t used an app in three months, the app will lose the ability to clear your data without you reconfirming it, and a number of things like that. So, that’s kind of category 1 going forward. And again, the good news there is that as of three or four years ago, new apps weren’t able to do what happened here. So this is largely ... this issue is resolved going forward for a while.
Then there’s going backwards, which is before 2014, what are all the apps that got access to more data than people would be comfortable with? And which of them were good actors, like legitimate companies, good intent developers, and which one of them were scams, right? Like, what Aleksandr Kogan was doing, basically using the platform to gather a bunch of information, sell it or share it in some sketchy way. So what we announced there is, we’re going to do a full investigation of every single app that had access to a large amount of people’s data, before 2014 when we lost out the platform, and if we detect anything suspicious, we’re basically going to send in a team to do a full forensic audit, to confirm that no Facebook data is being used in an improper way.
And of course, any developer that isn’t comfortable with that, then we’ll just ban them from the platform. If we find anything that is bad, then we’ll of course also ban the developer, but we will then also notify and tell people, everyone whose data has been affected. Which we’re also going to do here.
KS: So that begs the question ... this started off in 2007, 2008 when you were [launching] Facebook Connect, a lot of this stuff started very early, and I remember being at that event where you talked about this. Open and sharing, and it was helpful to growing your platform, obviously. Why wasn’t this done before? What’s in the mentality of your engineers of Facebook where you didn’t suspect this could be a problem?
Well, I don’t think it’s engineers.
KS: Well, whatever. People [at Facebook].
So, in 2007 we launched the platform.
KS: Yep.
The vision, if you remember is to help make apps social.
KS: Right.
So, the examples we had were, you know, your calendar should have your friend’s birthday. Your address book should have your friend’s picture. In order to do that, you basically need to make it so a person can log into an app and not just port their own data over, but also be able to bring some data from their friends as well. That was the vision, and a bunch of good stuff got created. There were a bunch of games that people liked. Music experiences, things like Spotify Travel, you know, things like Airbnb they were using it. But there was also a lot of scammy stuff.
There’s this values tension playing out between the value of data portability, right? Being able to take your data and some social data ... To be able to create new experiences on the one hand, and privacy on the other hand, and just making sure that everything is as locked down as possible.
You know, frankly, I just got that wrong. I was maybe too idealistic on the side of data portability, that it would create more good experiences. And it created some, but I think what the clear feedback was from our community was that people value privacy a lot more. And they would rather have their data locked down and be sure that nothing bad will ever happen to it than be able to easily take it and have social experiences in other places. So, over time, we have been just kind of narrowing it down. And 2014 was a really big ...
KS: I get that. 2014 you absolutely did that. But I’m talking about the ... You know — and I’ve argued with [Facebook executives] about this — this anticipation of problems, of possible bad actors on this platform. Do you all have enough mentality, or do you not see ... I want to understand what happens within Facebook that you don’t see that this is so subject to abuse. How do you think about that, and what is your responsibility?
Yeah. Well, I hope we’re getting there. I think we remain idealistic, but I think also understand what our responsibility is to protect people now. And I think the reality is is that in the past we had a good enough appreciation of some of this stuff. And some of it was that we were a smaller company, so some of the issues and some of these bad actors just targeted us less, because we were smaller. But we certainly weren’t in a target of nation states trying to influence elections back when we only had 100 million people in the community.
But I do think part of this comes from these idealistic values of openness and data portability and things that I think the tech community holds really dear, but are in some conflict with some of these other values, are in protecting people privately, right? And a lot of the most sensitive issues that we faced today are conflicts between our real values, right? Freedom of speech and hate speech and offensive content. Where is the line, right? And the reality is that different people are drawn to different places, we serve people in a lot of countries around the world, a lot of different opinions on that.
KS: Right, so where’s your opinion right now? Sorry to interrupt.
On that one specifically?
KS: Yeah.
You know, what I would really like to do is find a way to get our policies set in the way that reflects the values of the community so I’m not the one making those decisions. Right? I feel fundamentally uncomfortable sitting here in California at an office, making content policy decisions for people around the world. So there are going to be things that we never allow, right, like terrorist recruitment and ... We do, I think, in terms of the different issues that come up, a relatively very good job on making sure that terrorist content is off the platform. But things like where is the line on hate speech? I mean, who chose me to be the person that ...
KS: Well. Okay ...
I have to, because [I lead Facebook], but I’d rather not.
KS: I’m going to push back on that, because values are what we argue about. And companies have values, and they have, you know, the New York Times has a set of values that they won’t cross and they make decisions. Why are you so uncomfortable making those value decisions? You run the platform. It is more than just a benign platform that is neutral. It just isn’t. I don’t know, we can disagree on that, we obviously disagree on this. But why are you uncomfortable doing that?
Well, I just want to make the decisions as well as possible, and I think that there is likely a better process, which I haven’t figured out yet. So, for now, it’s my job, right? And I am responsible for it. But I just wish that there were a way ... a process where we could more accurately reflect the values of the community in different places. And then in the community standards, have that be more dynamic in different places. But I haven’t figured it out yet. So I’m just giving this as an example of attention that we debate internally, but clearly until we come up with a reasonable way to do that, that is our job, and I do well in that.
Kurt Wagner: Hey, Mark, this is Kurt. I’m curious, you talked about going back and trying to figure out if there were other developers that had used your API before 2014, and checking were there any other bad actors that maybe you guys missed at the time. I’m curious how you actually go about doing that, and if it’s actually possible at this point to go out and detect, you know, if someone collected data in 2012, if that data still exists.
Well, the short answer is the data isn’t on our servers so it would require us sending out forensic auditors to different apps. The basic process that we’ve worked out — and this is a lot of what we were trying to figure out over the last couple of days and why it took a little while to get this post out — is we do know all the apps that registered for Facebook and all the people who are on Facebook who register for those apps and have a log of the different data requests that the developer has made.
So we can get a sense of what are reputable companies, what are companies that were doing unusual things ... Like, that either requested data in spurts, or requested more data than it seemed like they needed to have. And anyone who either has a ton of data or something unusual, we’re going to take the next step of having them go through an audit. And that is not a process that we can control, they will have to sign up for it. But we’ll send in teams, who will go through their servers and just see how they’re handling data. If they still have access to data that they’re not supposed to, then we’ll shut them down and notify ... and tell everyone whose data was affected.
This is a complex process. It’s not going to be overnight. It’s going to be expensive for us to run, and it’s going to take a while. But look, given the situation here, that we had a developer that signed a legal certification saying that they deleted the data, now two years later we’re back here and it seems like they didn’t, what choice do we have? This is our responsibility to our community is to make sure that we go out and do this. So, even though it’s going to be hard and not something that our engineers can just do sitting in their offices here, I still think we have to go do this.
KW: Did you ever think of doing these kinds of audits before 2014? Or even when you got that signed contract from ... or, excuse me, signed statement I guess, from Cambridge Analytica, did you think, “Well, we need to actually go out and check to make sure that they’re telling us the truth.” Why didn’t you do this kind of stuff earlier, or did you think about doing this earlier?
In retrospect, it was clearly a mistake. Right? The basic chronology here is in 2015, a journalist from the Guardian pointed out to us that it seemed like the developer Aleksandr Kogan had sold shared data to Cambridge Analytica and a few other firms. So as soon as we learned that, we took down the app, and we demanded that Kogan, Cambridge Analytica and all the other folks give up the formal, legal certification that they didn’t have any other data. And, at the time, Cambridge Analytica told us that not only do we not have the data and it’s deleted, but so we actually never got access to raw Facebook data. Right? What they said was, this app that Kogan built, it was a personality quiz app, and instead of raw data they got access to some derived data, some personality scores for people. And they said that they used it in some models and it ended up not being useful so they just got rid of it.
So, given that, that they said that they never had the data and deleted what derivative data that they had, at the time it didn’t seem like we needed to go further on that. But look, in retrospect it was clearly a mistake. I’m explaining to you the situation at the time and the actions that we took, but I’m not trying to say it was the right thing to do. I think given what we know now, we clearly should have followed up, and we’re never going to make that mistake again.
I think we let the community down, and I feel really bad and I’m sorry about that. So that’s why we’re going to go and do these broad audits.
KS: All right, when you think about that idea of ... it’s not exactly a “mistakes were made” kind of argument, but you are kind of making that. That idea. I want to understand, what systems are going to be in place, but it’s sort of, you know, the horses are out of the barn door. Can you actually go get that data from them? Are you ... It’s everywhere, I would assume. I’ve been told by many, many people that have access to your data, I was thinking of companies like RockYou and all kinds of things from a million years ago that have a lot of your data ... Can you actually get it back? I don’t think you can. I can’t imagine you can.
Not always. But the goal isn’t to get the data back from RockYou. You know, people gave their data to RockYou. So RockYou has the right to have the data. What RockYou does not have the right to do is share the data or sell it to someone without people’s consent. And part of the audits and what we’re going to do is see whether those business practices were in place, and if so we can kind of follow that trail and make sure that developers who might be downstream of that comply or they’re going to get banned from our platform overall.
It isn’t perfect. But I do think that this is going to be a major deterrent going backwards. I think it will clean up a lot of data, and going forward the more important thing is just preventing this from happening in the first place, and that’s going to be solved by restricting the amount of data that developers can have access to. So I feel more confident that that’s going to work, starting in 2014 and going forward. Again, for the last few years already it hasn’t been possible for developers to get access to that much.
KS: Let me ask just two more quick questions.
[Here, there is logistical cross-talk with a person on his staff, since Zuckerberg had to head to an employee meeting.]
All right, I’m talking to you while walking over there for Q&A.
KS: All right, the cost of this? And are you going to testify in front of Congress? And if so, when?
You know, I’m open to doing that. I think that the way that we look at testifying in front of Congress is that ... We actually do this fairly regularly, right? There are high-profile ones like the Russian investigation, but there are lots of different topics that Congress needs and wants to know about. And the way that we approach it is that our responsibility is to make sure that they have access to all the information that they need to have. So I’m open to doing it.
KS: What is “open”? Is that a “yes” or a “no”?
KS: They want you, Mark.
Well look, I am not 100 percent sure that’s right. But the point of congressional testimony is to make sure that Congress gets the data in the information context that they need. Typically, there is someone at Facebook whose full-time job is going to be focused on whatever the area is. Whether it’s legal compliance, or security. So I think most of the time if what they’re really focused on is getting access to the person who is going to be most knowledgeable on that thing, there will be someone better. But I’m sure that someday, there will be a topic that I am the person who has the most knowledge on it, and I would be happy to do it then.
KW: Mark, can you give us a sense of the timing and cost for this? Like, the audits that you’re talking about. Is there any sense of how quickly you could do it and what kind of cost it would be to the company?
I think it depends on what we find. But we’re going to be investigating and reviewing tens of thousands of apps from before 2014, and assuming that there’s some suspicious activity we’re probably going to be doing a number of formal audits, so I think this is going to be pretty expensive. You know, the conversations we have been having internally on this is, “Are there enough people who are trained auditors in the world to do the number of audits that we’re going to need quickly?” But I think this is going to cost many millions of dollars and take a number of months and hopefully not longer than that in order to get this fully complete.
KS: Okay, last question Mark, and then you can go. How badly do you think Facebook has been hurt by this, and you yourself, the reputation of Facebook?
I think it’s been a pretty big deal. The No. 1 thing that people care about is privacy and the handling of their data. You know, if you think about it, the most fundamental thing that our services are, whether it’s Facebook or Whatsapp or Instagram, is this question of, “Can I put content into it?” Right? Whether it’s a photo or a video or a text message. And will that go to the people I want to send it to and only those people? And whenever there is a breach of that, that undermines the fundamental point of these services. So I think it’s a pretty big deal, and that’s why we’re trying to make sure we fully understand what’s going on, and make sure that this doesn’t happen again. I’m sure there will be different mistakes in the future, but let’s not make this one again.
KS: Yes, let’s not. Okay, Mark, I really appreciate you talking to us.
KW: Okay, Mark.
KS: Thank you so much, I know you have to talk to your employees ...
I’m walking into my Q&A now. All right, see ya.