People concerned about privacy often try to be “careful” online. They stay off social media, or if they’re on it, they post cautiously. They don’t share information about their religious beliefs, personal life, health status or political views. By doing so, they think they are protecting their privacy.
But they are wrong. Because of technological advances and the sheer amount of data now available about billions of other people, discretion no longer suffices to protect your privacy. Computer algorithms and network analyses can now infer, with a sufficiently high degree of accuracy, a wide range of things about you that you may have never disclosed, including your moods, your political beliefs, your sexual orientation and your health.
There is no longer such a thing as individually “opting out” of our privacy-compromised world.
The basic idea of data inference is not new. Magazine subscriber lists have long been purchased by retailers, charities and politicians because they provide useful hints about people’s views. A subscriber to The Wall Street Journal is more likely to be a Republican voter than is a subscriber to The Nation, and so on.
But today’s technology works at a far higher level. Consider an example involving Facebook. In 2017, the newspaper The Australian published an article, based on a leaked document from Facebook, revealing that the company had told advertisers that it could predict when younger users, including teenagers, were feeling “insecure,” “worthless” or otherwise in need of a “confidence boost.” Facebook was apparently able to draw these inferences by monitoring photos, posts and other social media data. (...)
It is worth stressing that today’s computational inference does not merely check to see if Facebook users posted phrases like “I’m depressed” or “I feel terrible.” The technology is more sophisticated than that: Machine-learning algorithms are fed huge amounts of data, and the computer program itself categorizes who is more likely to become depressed.
Consider another example. In 2017, academic researchers, armed with data from more than 40,000 Instagram photos, used machine-learning tools to accurately identify signs of depression in a group of 166 Instagram users. Their computer models turned out to be better predictors of depression than humans who were asked to rate whether photos were happy or sad and so forth.
Such tools are already being marketed for use in hiring employees, for detecting shoppers’ moods and predicting criminal behavior. Unless they are properly regulated, in the near future we could be hired, fired, granted or denied insurance, accepted to or rejected from college, rented housing and extended or denied credit based on facts that are inferred about us.
This is worrisome enough when it involves correct inferences. But because computational inference is a statistical technique, it also often gets things wrong — and it is hard, and perhaps impossible, to pinpoint the source of the error, for these algorithms offer little to no insights into how they operate. What happens when someone is denied a job on the basis of an inference that we aren’t even sure is correct?
Another troubling example of inference involves your phone number. It is increasingly an identifier that works like a Social Security number — it is unique to you. Even if you have stayed off Facebook and other social media, your phone number is almost certainly in many other people’s contact lists on their phones. If they use Facebook (or Instagram or WhatsApp), they have been prompted to upload their contacts to help find their “friends,” which many people do.
Once your number surfaces in a few uploads, Facebook can place you in a social network, which helps it infer things about you since we tend to resemble the people in our social set. (Facebook even keeps “shadow” profiles of nonusers and deploys “tracking pixels” situated all over the web — not just on Facebook — that transmit information about your behavior to the company.)
by Zeynep Tufekci, NY Times | Read more:
Image: Alexis Beauclair
But they are wrong. Because of technological advances and the sheer amount of data now available about billions of other people, discretion no longer suffices to protect your privacy. Computer algorithms and network analyses can now infer, with a sufficiently high degree of accuracy, a wide range of things about you that you may have never disclosed, including your moods, your political beliefs, your sexual orientation and your health.
There is no longer such a thing as individually “opting out” of our privacy-compromised world.The basic idea of data inference is not new. Magazine subscriber lists have long been purchased by retailers, charities and politicians because they provide useful hints about people’s views. A subscriber to The Wall Street Journal is more likely to be a Republican voter than is a subscriber to The Nation, and so on.
But today’s technology works at a far higher level. Consider an example involving Facebook. In 2017, the newspaper The Australian published an article, based on a leaked document from Facebook, revealing that the company had told advertisers that it could predict when younger users, including teenagers, were feeling “insecure,” “worthless” or otherwise in need of a “confidence boost.” Facebook was apparently able to draw these inferences by monitoring photos, posts and other social media data. (...)
It is worth stressing that today’s computational inference does not merely check to see if Facebook users posted phrases like “I’m depressed” or “I feel terrible.” The technology is more sophisticated than that: Machine-learning algorithms are fed huge amounts of data, and the computer program itself categorizes who is more likely to become depressed.
Consider another example. In 2017, academic researchers, armed with data from more than 40,000 Instagram photos, used machine-learning tools to accurately identify signs of depression in a group of 166 Instagram users. Their computer models turned out to be better predictors of depression than humans who were asked to rate whether photos were happy or sad and so forth.
Such tools are already being marketed for use in hiring employees, for detecting shoppers’ moods and predicting criminal behavior. Unless they are properly regulated, in the near future we could be hired, fired, granted or denied insurance, accepted to or rejected from college, rented housing and extended or denied credit based on facts that are inferred about us.
This is worrisome enough when it involves correct inferences. But because computational inference is a statistical technique, it also often gets things wrong — and it is hard, and perhaps impossible, to pinpoint the source of the error, for these algorithms offer little to no insights into how they operate. What happens when someone is denied a job on the basis of an inference that we aren’t even sure is correct?
Another troubling example of inference involves your phone number. It is increasingly an identifier that works like a Social Security number — it is unique to you. Even if you have stayed off Facebook and other social media, your phone number is almost certainly in many other people’s contact lists on their phones. If they use Facebook (or Instagram or WhatsApp), they have been prompted to upload their contacts to help find their “friends,” which many people do.
Once your number surfaces in a few uploads, Facebook can place you in a social network, which helps it infer things about you since we tend to resemble the people in our social set. (Facebook even keeps “shadow” profiles of nonusers and deploys “tracking pixels” situated all over the web — not just on Facebook — that transmit information about your behavior to the company.)
by Zeynep Tufekci, NY Times | Read more:
Image: Alexis Beauclair
