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A tech insider’s data dreams will resonate with the like-minded but neglect issues of access and equality

Data For the People: How to Make Our Post-Privacy Economy Work for You

Andreas Weigend
Basic Books
2017
320 pp.
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Andreas Weigend, former chief scientist of Amazon, has written a book, Data for the People: How to Make Our Post-Privacy Economy Work for You. It’s a techno-insider handbook for making the world a more efficient self-branding mechanism.

 

First things first. Who are “the people” that Weigend is addressing? The answer comes indirectly, through examples.

 

The book begins with a very short history of privacy, which talks about founding fathers and newspaper magnates but neglects to mention the history of privacy—or lack thereof—for anyone except wealthy and influential white men. Slaves, after all, had no expectation of privacy.

 

Weigend concludes that privacy is dead and that we should move on and take advantage of what all this surveillance and data offers us—namely, an opportunity to control our personal brand. Weigend manages to be both aware of the inner workings of the big data community and totally uncritical of it, at times a propagandist for it. The strongest example of this can be found in what he calls the “honest signal,” a term he borrowed from social scientists. In the social sciences, the term refers to a signal that is costly to create, like the tail of a peacock, which (honestly) signals health and vigor.

 

WESLEY TOLHURST/ISTOCKPHOTO

Like the plume of feathers that serves as a proxy for a peacock’s vitality, our online behavior can serve as an “honest signal” of our true interests, argues Weigend.

Weigend defines an honest signal to be a trait revealed by people’s behavior online that may be contrary to their reported preferences. Participants in a poll may report greater attention to geopolitics than popular culture, for example, but an honest data signal might reveal that they click on articles about Kim Kardashian while scrolling past updates on Ukraine.
The problem is that this term doesn’t translate. A click on an article is simply not as costly as growing a plume of feathers. And any inference of the clicker’s intent is likely a statistically noisy guess. A click can serve as a weak proxy for interest or engagement, but that’s about it. Of course, “honest signal” certainly sounds better than “weak proxy.”

 
Weigend’s main goal is to help us control and massage our online reputations by showing us how to generate valuable honest signals. Managers shouldn’t take a potential employee’s word that they know important people in an industry, he maintains; instead, they should demand access to that applicant’s complete LinkedIn graph. Once on the job, employees should be amenable to being tracked in minute ways—with sensors and sociometric badges, for example—so that employers can measure and promote efficiency.

 
Weigend suggests that readers should tie their reputation to trusted people in their respective networks. He acknowledges that this could mean unfriending someone who might lower your reputation score, “much like a person might sever ties with a disreputable character in town,” but all’s fair in the fight for a stellar online status. Instead of establishing credit histories, he suggests that potential borrowers should simply proffer their SAT scores and alma maters so that their future earnings can be extrapolated. But here’s the creepiest example: On the topic of organ donations, Weigend suggests that we could “use social data to estimate the value of each patient’s life, predicting with precision how much an additional year of life is worth to her family and society.” Talk about blindly trusting data.

 

Throughout the book, Weigend steadfastly ignores questions of class, discrimination, and access. For him, the world is an objective and fair meritocracy where all data-driven beings can compete equally. And although he calls for explanations of how credit scores are created and what experiments are being done on Facebook, his goal seems to be helping people in the know game such algorithms for their own benefit.

 

In fairness to Weigend, he seems like a true believer. He doesn’t just trust data, he actually seems to want to be funneled through life by its associated predictive analytics. Here’s someone who dives headfirst into the surveillance state, who wore Google Glass almost constantly for the better part of a year, and who bemoans local privacy laws that prevent him from installing surveillance cameras in his mother’s home in Germany.
Weigend has written the ultimate guide to making things better for engineers and data scientists, but not so for the average person. His naïveté is startling and alarming, and although he provides a service in explaining his vision of the future of big data, it’s not enough to mitigate the many terrible suggestions that are made throughout this book.

 

About the author

The reviewer is the author of Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (Crown, 2016).