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Do Not Track

A personalized documentary series about privacy and the web economy.

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S01E02 : Breaking Ad

Accepting cookies is a part of our digital life.
If we said no, would the Internet still work? Let’s trace the economic origins of online tracking.

E1 - Tracking E3 - Social Networks E4 - Mobile E7 - Future of Tracking Must Read

Tim Cook: Silicon Valley’s most successful companies are selling you out

Apple CEO Tim Cook has made no secret of his disdain for online services that ask you to trade highly personal data for convenience — a trade that describes most big advertising-supported technology companies. But last night, in some of his strongest comments to date, Cook said the erosion of privacy represents a threat to the American way of life. Cook spoke at a dinner in Washington, DC, hosted by the Electronic Privacy Information Center, which honored him as a “champion of freedom” for his leadership at Apple.

“Our privacy is being attacked on multiple fronts,” Cook said in a speech that he delivered remotely, according to EPIC. “I’m speaking to you from Silicon Valley, where some of the most prominent and successful companies have built their businesses by lulling their customers into complacency about their personal information. They’re gobbling up everything they can learn about you and trying to monetize it. We think that’s wrong. And it’s not the kind of company that Apple wants to be.”

— Read on The Verge
E1 - Tracking Must Read

Sharing Data, but Not Happily

Should consumers be able to control how companies collect and use their personal data?

At a dinner honoring privacy advocates this week in Washington, Timothy D. Cook, the chief executive of Apple, gave a speech in which he endorsed this simple idea. Yet his argument leveled a direct challenge to the premise behind much of the Internet industry — the proposition that people blithely cede their digital bread crumbs to companies in exchange for free or reduced-priced services subsidized by advertising.

“You might like these so-called free services,” Mr. Cook said during the event held by EPIC, a nonprofit research center. “But we don’t think they’re worth having your email or your search history or now even your family photos data-mined and sold off for God knows what advertising purpose.”

— Read on New York Times
E7 - Future of Tracking Must Read

Mark Zuckerberg, Let Me Pay for Facebook

Facebook. Instagram. Google. Twitter. All services we rely on — and all services we believe we don’t have to pay for. Not with cash, anyway. But ad-financed Internet platforms aren’t free, and the price they extract in terms of privacy and control is getting only costlier.

A recent Pew Research Center poll shows that 93 percent of the public believes that “being in control of who can get information about them is important,” and yet the amount of information we generate online has exploded and we seldom know where it all goes.

— Read on New York Times
E7 - Future of Tracking Must Read

The Future Is A Confidence Trick

Prediction is an industry, and its product is a persuasive set of hopes and fears that we’re trained or convinced to agree upon.  It’s a confidence trick.  And its product comes so thick and fast that, like a plothole in an action movie, we’re carried on past the obvious failures and the things that didn’t even make sense if we had more than five seconds to think about them.

— Read on Waren Ellis

S01E03 : Like Mining

A couple of likes on Facebook, that can’t say much about you, right? Wrong.
The German blogger and TV-presenter Richard Gutjahr welcomes you to the unbelievable world of online-profiling.

E6 - Filter Bubble Must Read

The Hypocrisy of the Internet Journalist

It’s been hard to make a living as a journalist in the 21st century, but it’s gotten easier over the last few years, as we’ve settled on the world’s newest and most lucrative business model: invasive surveillance. News site webpages track you on behalf of dozens of companies: ad firms, social media services, data resellers, analytics firms — we use, and are used by, them all.

For years, as a regular writer at Wired, I watched this system grow up with unease. I watched more companies put tracking cookies and scripts in every article I wrote. As my career went on, that list kept getting longer. Unlike most of the people I worked with at Wired, I understood the implications of what we were doing. Most journalists have no idea how extensive the system their readers are sold into is, but I have no such excuse. Long before I was a journalist, at the very dawn of the era of the web, I worked in database marketing — what’s more commonly called analytics now.

I got into it from the internet side, but for marketers who built databases of consumer information, the web was love at first sight. The introduction of the browser cookie was a transcendent moment in data collection. It was like the first time a kid at Hogwarts used their wand. You knew it was big, but how big? All you could say is “This will be bigger than I can imagine now.” — and that’s what I told people.

— Read on Medium
E3 - Social Networks E6 - Filter Bubble Must Read

Israel, Gaza, War & Data – The Art of Personalizing Propaganda

The better we get at modeling user preferences, the more accurately we construct recommendation engines that fully capture user attention. In a way, we are building personalized propaganda engines that feeds users content which makes them feel good and throws away the uncomfortable bits. We used to be able to hold media accountable for misinforming the public. Now we only have ourselves to blame.

— Read on Global Voices Online
E5 - Big Data Must Read

Big Data: The Categorizing Machine

You want to know the habits of mobile phone users? Big Data. You want to reach a targeted clientele on the Web? Big Data. You want to decode the secrets of the latest on Netflix, or learn where to fix potholes in a neighbourhood? Big Data! All you need is a good algorithm, and a decent quantity of data, and the companies that analyze Big Data promise to find all sorts of answers to our questions. But who’s asking these questions? And can we trust algorithms to make decisions?

2015 is the year of Big Data. The concept of Big Data has existed for forty years already, but according to Forbes, this is the year that marks Big Data’s entry into the business world and governance. A bunch of companies are retuning their business models to reap profits from a new source of wealth: our personal data.

Big Data Mashup

Statistical analysis has always been with us. By taking surveys, or by calculating selected answers in a census form, we can estimate, more or less, the probability that a candidate will be elected, the number of car accidents annually, or even the type of individual most likely to reimburse a loan. Mistakes can be made, but numbers help uncover trends. And based on those trends, we hopefully make the right decisions.

Nowadays, we produce trends using quintillions of data points. Add this to the information collected by institutions and credit companies, browsing history tracked by cookies (episode 2), the data from our mobile phones (episode 4), 50 million photos, 40 million Tweets, and billions of documents exchanged daily. Now add the data produced by sports bracelets, “smart” objects and gadgets, and you’ll understand why “Big” is the right adjective to describe the vast expanse of available information.

However, the true revolutionary aspect of Big Data isn’t so much a question of its size, as it is the way in which all of this data can be mixed. Beyond the things it says about us (or despite us), it is the correlation and mixing of personal information that allow the behaviours of users to be predicted.

Being able to know what you say online? Who cares! But knowing the words used, and with whom you are exchanging, on what network, and at what time? Now that’s a moneymaker.

Categorization For The Win

With something as simple as a postal code, for example, average consumer income can be predicted. The Esri and Claritas agencies even claim to be able to deduce education level, lifestyle, family composition, and consumer habits from this one piece of information. Target made headlines in 2012 when it predicted a teenager’s pregnancy, before her parents were aware, based on the type of lotions, vitamins, and color of items purchased.

For these algorithms to work properly, individuals have to be put into increasingly more precise categories. And that is where discrimination lurks. Because we don’t always fit easily into a pigeonhole.

Predictions and Discrimination

As Kate Crawford stated when she was interviewed in episode 5, it is minorities, and those who are already discriminated against, that are the most affected by prediction errors. The more an individual corresponds to the “norm”, or to a predetermined category, the easier it is to take their data into consideration. But what happens when we are on the margins? What happens to those that don’t behave the way Amazon, Google, or Facebook predicts?

Facebook recently angered many of its users by strictly enforcing a section of their Terms and Conditions which insists people use their real names within the service. The purpose, says the company, was to provide a safer environment and limit hateful posts. What they didn’t account for was the deletion of accounts from the transgendered, indigenous and survivors of domestic violence whose accounts weren’t held under “real” names. This violated not only the individual rights but also the privacy of these users.

And what about prejudices and discrimination that algorithms only serve to reinforce? In 2014, Chicago police rang the doorbell of a 22 year old young man named Robert McDaniels. “We’re watching you” said one of the officers. This was the result of an algorithm developed by the Illinois Institute of Technology placing him on a list of 400 potential criminals because of crime data about his neighbourhood, the intersections where crimes occurred in the past, and his degrees of separation from people involved in crimes. It’s like science fiction. And if there was a misconception, how would it be repaired?

Take the Test

We’re not going to lie to you: it’s difficult, if not impossible, to find out how we are categorized – and even harder to avoid it altogether. It all depends on the company, the algorithm, and the information that they are after. However, some tools can give us a glimpse into the ways in which the Web categorizes us:

  • The extension called Floodwatch (link) allows us to have a quick look at all of the advertisements that target us personally over a long time period. Handy for retracing our browsing practices and how they affect our categorization!
  • Even simpler? If you are logged in to your Google account – Go to the Ad Parameters page – Does this profile resemble you? It’s up to you if you want to correct it, or you could just adopt this new identity as a form of camouflage.

Sandra Rodriguez

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