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.
At Facebook’s request I have again deactivated the *official* version of the extension. Furthermore, Facebook has deactivated location sharing from the desktop webpage so the extension will not work. However, it seems locations are still being shared on the mobile app and sharing is still enabled by default.
What if your credit scores were assessed based on your Facebook likes and your health insurance plan on your Netflix history? Do Not Track, a web-based documentary series about Internet privacy and data collection, shows that our digital footprint may soon become an even more integral part of our lives.
Directed by Brett Gaylor, this interactive series is meant as a warning call for all those who believe their browsing history to be of little importance. By accessing viewers’ IP address, favorite websites, and Facebook accounts, the web doc personalizes its content, providing viewers with geographically relevant GIFs, different narrators, and even the Big Five personality traits that apply to them. Consequently, no two screenings are the same, and we get an astonishing real-time look at how our data is being tracked, analyzed, and sold.
Each episode is accompanied by a selection of articles and videos relevant to the topic at hand, allowing us to further our understanding of issues such as the tracking industry and its economic origins, cookie files, and online profiling.
Is it possible to keep the Internet from realizing that you’re pregnant? That’s the question Princeton sociology professor Janet Vertesi set out to answer in 2013 when she discovered that she was expecting. Her nine-month experiment required her to think like a criminal about how she could go about leaving no trace of her bundle of joy in any of her email activity. She had to call family and friends and tell them not to talk about the pregnancy on Facebook. She and her husband bought baby products — like prenatal vitamins — in person in cash. When she did buy things online, she used Tor to mask her IP address and conceal her identity while browsing, bought items with gift cards from Rite Aid, and had them shipped to an Amazon locker so her home address wouldn’t be associated with the orders.
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.
At an 18th-century mansion in England’s countryside last week, current and former spy chiefs from seven countries faced off with representatives from tech giants Apple and Google to discuss government surveillance in the aftermath of Edward Snowden’s leaks.
The three-day conference, which took place behind closed doors and under strict rules about confidentiality, was aimed at debating the line between privacy and security.
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:
Sandra Rodriguez
Canada and its spying partners exploited weaknesses in one of the world’s most popular mobile browsers and planned to hack into smartphones via links to Google and Samsung app stores, a top secret document obtained by CBC News shows.
The 2012 document shows that the surveillance agencies exploited the weaknesses in certain mobile apps in pursuit of their national security interests, but it appears they didn’t alert the companies or the public to these weaknesses. That potentially put millions of users in danger of their data being accessed by other governments’ agencies, hackers or criminals.
A WOMAN AT a gym tells her friend she pays rent higher than $2,000 a month. An ex-Microsoft employee describes his work as an artist to a woman he’s interviewing to be his assistant—he makes paintings and body casts, as well as something to do with infrared light that’s hard to discern from his foreign accent. Another man describes his gay lover’s unusual sexual fetish, which involves engaging in fake fistfights, “like we were doing a scene from Batman Returns.”
These conversations—apparently real ones, whose participants had no knowledge an eavesdropper might be listening—were recorded and published by the NSA. Well, actually no, not the NSA, but an anonymous group of anti-NSA protestors claiming to be contractors of the intelligence agency and launching a new “pilot program” in New York City on its behalf. That spoof of a pilot program, as the prankster provocateurs describe and document in videos on their website, involves planting micro-cassette recorders under tables and benches around New York city, retrieving the tapes and embedding the resulting audio on their website: Wearealwayslistening.com.
We’re in the year 2049. The world has seen an apocalyptic breakdown of all media known to man. There is no more human communication outside the global network of Facebook. A protocol of the end of mass media as we know it.
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