Facebook’s Model Users

DontAnthropomorphizePeopleFacebook’s recent pscyhology experiment has raised difficult questions about the ethical standards of data-driven companies, and the universities that collaborate with them. We are still learning exactly who did what before publication. Some are wisely calling for a “People’s Terms of Service” agreement to curb further abuses. Others are more focused on the responsibility to protect research subjects. As Jack Balkin has suggested, we need these massive internet platforms to act as fiduciaries.

The experiment fiasco is just the latest in a long history of ethically troubling decisions at that firm, and several others like it. And the time is long past for serious, international action to impose some basic ethical limits on the business practices these behemoths pursue.

Unfortunately, many in Silicon Valley still barely get what the fuss is about. For them, A/B testing is simply a way of life. Using it to make people feel better or worse is a far cry from, say, manipulating video poker machines to squeeze a few extra dollars out of desperate consumers. “Casino owners do that all the time!”, one can almost hear them rejoin.

Yet there are some revealing similarities between casinos and major internet platforms. Consider this analogy from Rob Horning:

Social media platforms are engineered to be sticky — that is, addictive, as Alexis Madrigal details in [a] post about the “machine zone.” . . . Like video slots, which incite extended periods of “time-on-machine” to assure “continuous gaming productivity” (i.e. money extraction from players), social-media sites are designed to maximize time-on-site, to make their users more valuable to advertisers (Instagram, incidentally, is adding advertising) and to ratchet up user productivity in the form of data sharing and processing that social-media sites reserve the rights to.

That’s one reason we get headlines like “Teens Can’t Stop Using Facebook Even Though They Hate It.” There are sociobiological routes to conditioning action. The platforms are constantly shaping us, based on sophisticated psychological profiles.

For Facebook to continue to meet Wall Street’s demands for growth, its user base must grow and/or individual users must become more “productive.” Predictive analytics demands standardization: forecastable estimates of revenue-per-user. The more a person clicks on ads and buys products, the better. Secondarily, the more a person draws other potential ad-clickers in–via clicked-on content, catalyzing discussions, crying for help, whatever–the more valuable they become to the platform. The “model users” gain visibility, subtly instructing by example how to act on the network. They’ll probably never attain the notoriety of a Lei Feng, but the Republic of Facebookistan gladly pays them the currency of attention, as long as the investment pays off for top managers and shareholders.

As more people understand the implications of enjoying Facebook “for free“–i.e., that they are the product of the service–they also see that its real paying customers are advertisers. As Katherine Hayles has stated, the critical question here is: “will ubiquitous computing be coopted as a stalking horse for predatory capitalism, or can we seize the opportunity” to deploy more emancipatory uses of it?  I have expressed faith in the latter possibility, but Facebook continually validates Julie Cohen’s critique of a surveillance-innovation complex.

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