Facebook is now more forthright about this. In a response to the recent controversy, Facebook data scientist Adam Kramer wrote, "The goal of all of our research at Facebook is to learn how to provide a better service...We were concerned that exposure to friends' negativity might lead people to avoid visiting Facebook. We didn't clearly state our motivations in the paper."
Facebook’s former head of data science Cameron Marlow offers, “Our goal is not to change the pattern of communication in society. Our goal is to understand it so we can adapt our platform to give people the experience that they want.”
But data scientists don’t just produce knowledge about observable, naturally occurring phenomena; they shape outcomes. A/B testing and routinized experimentation in real time are done on just about every major website in order to optimize for certain desired behaviors and interactions. Google designers infamously tested up to 40 shades of blue. Facebook has already experimented with the effects of social pressure in getting-out-the-vote, raising concerns about selective digital gerrymandering. What might Facebook do with its version of this research? Perhaps it could design the News Feed to show us positive posts from our friends in order to make us happier and encourage us to spend more time on the site? Or might Facebook show us more sad posts, encouraging us to spend more time on the site because we have more to complain about?
Should we think of commercial data science as science? When we conflate the two, we assume companies are accountable for producing generalizable knowledge and we risk according their findings undue weight and authority. Yet when we don’t, we risk absolving practitioners from the rigor and ethical review that grants authority and power to scientific knowledge.
Facebook has published a paper in an attempt to contribute to the larger body of social science knowledge. But researchers today cannot possibly replicate Facebook's experiment without Facebook's cooperation. The worst outcome of this debacle would be for Facebook to retreat and avoid further public relations fiascos by keeping all its data science research findings internal. Instead, if companies like Facebook, Google, and Twitter are to support an open stance toward contributing knowledge, we need researchers with non-commercial interests who can run and replicate this research outside of the platform's influence.
Facebook sees its users not as a population of human subjects, but as a consumer public. Therefore, we—that public and those subjects—must ask the bigger questions. What are the claims that data science makes both in industry and academia? What do they say about the kinds of knowledge that our society values?
We need to be more critical of the production of data science, especially in commercial settings. The firms that use our data have asymmetric power over us. We do them a favor unquestioningly accepting their claims to the prestige, expertise, and authority of science as well.
Ultimately, society’s greatest concerns with science and technology are ethical: Do we accept or reject the means by which knowledge is produced and the ends to which it is applied? It’s a question we ask of nuclear physics, genetic modification—and one we should ask of data science.