Source: U.S. Senate Testimony | Idle Words, by Maciej Cegłowski
RE: U.S. Senate hearing on “Privacy Rights and Data Collection in a Digital Economy.”, by The Committee on Banking, Housing, and Urban Affairs
The sudden ubiquity of this architecture of mass surveillance, and its enshrinement as the default business model of the online economy, mean that we can no longer put off hard conversations about the threats it poses to liberty.
Adding to this urgency is the empirical fact that, while our online economy depends on the collection and permanent storage of highly personal data, we do not have the capacity to keep such large collections of user data safe over time.
While many individual data breaches are due to negligence or poor practices, their overall number reflects an uncomfortable truth well known to computer professionals—that our ability to attack computer systems far exceeds our ability to defend them, and will for the foreseeable future.
In the regulatory context, discussion of privacy invariably means data privacy—the idea of protecting designated sensitive material from unauthorized access.
It is true that, when it comes to protecting specific collections of data, the companies that profit most from the surveillance economy are the ones working hardest to defend them against unauthorized access.
But there is a second, more fundamental sense of the word privacy, one which until recently was so common and unremarkable that it would have made no sense to try to describe it.
That is the idea that there exists a sphere of life that should remain outside public scrutiny, in which we can be sure that our words, actions, thoughts and feelings are not being indelibly recorded. This includes not only intimate spaces like the home, but also the many semi-private places where people gather and engage with one another in the common activities of daily life—the workplace, church, club or union hall.
The tension between these interpretations of what privacy entails, and who is trying to defend it, complicates attempts to discuss regulation.
Tech companies will correctly point out that their customers have willingly traded their private data for an almost miraculous collection of useful services, services that have unquestionably made their lives better, and that the business model that allows them to offer these services for free creates far more value than harm for their customers.
Consumers will just as rightly point out that they never consented to be the subjects in an uncontrolled social experiment, that the companies engaged in reshaping our world have consistently refused to honestly discuss their business models or data collection practices, and that in a democratic society, profound social change requires consensus and accountability.
While it is too soon to draw definitive conclusions about the GDPR, there is a tension between its concept of user consent and the reality of a surveillance economy that is worth examining in more detail.
A key assumption of the consent model is any user can choose to withhold consent from online services. But not all services are created equal—there are some that you really can’t say no to.
The latent potential of the surveillance economy as a toolkit for despotism cannot be exaggerated. The monitoring tools we see in repressive regimes are not ‘dual use’ technologies—they are single use technologies, working as designed, except for a different master.
Also: Think You’re Discreet Online? Think Again | Opinion | The New York Times, by Zeynep Tufekci
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.
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.