The Beneficence of Mobs: A Facebook Apologia

Academics, Data

Last week, the Proceedings of the National Academy of Science (PNAS) published a study that conducted a large-scale experiment on Facebook. The authors of the study included an industry researcher from Facebook as well as academics at the University of California, San Francisco and Cornell University. The study employed an experimental design that reduced the amount of positive or negative emotional content in 689,000 Facebook users’ news feeds to test whether emotions are contagious. The study has since spawned a substantial controversy about the methods used, extent of its regulation by academic institutions’ review board, the nature of participants’ informed consent, the ethics of the research design itself, and the need for more explicit opt-in procedures.

In the face of even-tempered thinking from a gathering mob, I want to defend the execution and implications of this study. Others have also made similar arguments [1,2,3], I guess I’m just a slow blogger. At the outset, I want to declare that I have no direct stake in the outcome of this brouhaha. However, I do have professional and personal relationships with several members of the Facebook Data Science team (none of whom are authors on the study), although the entirety of this post reflects only public information and my opinions alone.

First, as is common in the initial reporting surrounding on scientific findings, there was some misinformation around the study that was greatly magnified. These early criticisms claimed the authors mis-represented the size of the observed effects (they didn’t) or the research wasn’t reviewed by the academic boards charged with human subjects protection (it was). There is likewise a pernicious tendency for the scientific concept of experimental manipulation to be misinterpreted as the homophone implying deception and chicanery: there is no inherent maliciousness in randomly assigning participants to conditions for experimental study. Other reporting on the story has sensationalistically implied users were subjected to injections of negative emotional content so their resulting depression could be more fully quantified. In reality, the study actually only withheld either positive or negative content from users, which resulted in users seeing more of posts they would have seen anyway. In all of these, the hysteria surrounding a “Facebook manipulates your emotions” or is “transmitting anger” story got well ahead of any sober reading of the research reported by the authors in the paper.

Second on the substance of the research, there are still serious questions about the validity of methodological tools used , the interpretation of results, and use of inappropriate constructs. Prestigious and competitive peer-reviewed journals like PNAS are not immune from publishing studies with half-baked analyses. Pre-publication peer review (as this study went through) is important for serving as a check against faulty or improper claims, but post-publication peer review of scrutiny from the scientific community—and ideally replication—is an essential part of scientific research. Publishing in PNAS implies the authors were seeking both a wider audience and a heightened level of scrutiny than publishing this paper in a less prominent outlet. To be clear: this study is not without its flaws, but these debates, in of themselves, should not be taken as evidence that the study is irreconcilably flawed. If the bar for publication is anticipating every potential objection or addressing every methodological limitation, there would be precious little scholarship for us to discuss. Debates about the constructs, methods, results, and interpretations of a study are crucial for synthesizing research across disciplines and increasing the quality of subsequent research.

Third, I want to move to the issue of epistemology and framing. There is a profound disconnect in how we talk about the ways of knowing how systems like Facebook work and the ways of knowing how people behave. As users, we expect these systems to be responsive, efficient, and useful and so companies employ thousands of engineers, product managers, and usability experts to create seamless experiences.  These user experiences require diverse and iterative methods, which include A/B testing to compare users’ preferences for one design over another based on how they behave. These tests are pervasive, active, and on-going across every conceivable online and offline environment from couponing to product recommendations. Creating experiences that are “pleasing”, “intuitive”, “exciting”, “overwhelming”, or “surprising” reflects the fundamentally psychological nature of this work: every A/B test is a psych experiment.

Somewhere deep in the fine print of every loyalty card’s terms of service or online account’s privacy policy is some language in which you consent to having this data used for “troubleshooting, data analysis, testing, research,” which is to say, you and your data can be subject to scientific observation and experimentation. Whether this consent is “informed” by the participant having a conscious understanding of implications and consequences is a very different question that I suspect few companies are prepared to defend. But why does a framing of “scientific research” seem so much more problematic than contributing to “user experience”? How is publishing the results of one A/B test worse than knowing nothing of the thousands of invisble tests? They reflect the same substantive ways of knowing “what works” through the same well-worn scientific methods.

Fourth, there has been no substantive discussion of what the design of informed consent should look like in this context. Is it a blanket opt-in/out to all experimentation? Is consent needed for every single A/B iteration or only those intended for scientific research? Is this choice buried alongside all the other complex privacy buttons or are users expected to manage pop-ups requesting your participation? I suspect the omnipresent security dialogues that Windows and OS X have adopted to warn us against installing software have done little to reduce risky behavior. Does adding another layer of complexity around informed consent improve the current anxieties around managing complex privacy settings? How would users go about differentiating official requests for informed consent from abusive apps, spammers, and spoofers? Who should be charged with enforcing these rules and who are they in turn accountable to? There’s been precious little on designing more informed consent architectures that balance usability, platform affordances, and the needs of researchers.

Furthermore, we might also consider the  ethics of this nascent socio-technical NIMBYism. Researchers at Penn State have looked at the design of privacy authorization dialogues for social networks but found that more fine-grained control over disclosure reduced adoption levels.  We demand ever more responsive and powerful systems while circumscribing our contributions but demanding benefits from other’s contributions. I image the life of such systems would be poor, nasty, brutish, and short. Do more obtrusive interventions or incomplete data collection in the name conservative interpretations of informed consent promote better science and other public goods? What are the specific harms that we should strive to limit in these systems and how might we re-tailor 40 year old policies to these ends?

I want to wrap up by shifting the focus of this conversation from debates about a study that was already done to what should be done going forward. Some of the more extreme calls I’ve seen have advocated for academic societies or institutions to investigate and discipline the authors, others have called for embargoing studies using Facebook data from scholarly publication, and still others have encouraged Facebook employees to quit in protest of a single study.  All this manning of barricades strikes me as a grave over-reaction that could have calamitously chilling effects on several dimensions.  If our overriding social goal is to minimize real or potential harm to participants, what best accomplishes this going forward?

Certainly expelling Facebook from the “community of scholars” might damage its ability to recruit researchers. But are Facebook users really made safer by replacing its current crop of data scientists who have superlative social science credentials with engineers, marketers, and product managers trying to ride methodological bulls they don’t understand? Does Facebook have greater outside institutional accountability by closing down academic collaborations and shutting papers out from peer review and publication? Are we better able to know the potential influence Facebook wields over our emotions, relationships, and politics by discouraging them from publicly disclosing the tools they have developed?  Is raising online mobs to attack industry researchers conducive to starting dialogues to improve their processes for informed consent? Is publicly undermining other scientists the right strategy for promoting evidence-based policy-making in an increasingly hostile political climate?

Needless to say, this episode speaks for the need for rapprochement and sustained engagement between industry and academic researchers. If you care about research ethics, informed consent, and well-designed research, you want companies like Facebook deeply embedded within and responsible to the broader research community. You want the values of social scientists to influence the practice of data science, engineering,  user experience, and marketing teams. You want the campus to be open to visiting academic researchers to explore, collaborate, and replicate. You want industry research to be held to academia’s more stringent standards of human subjects protection and regularly shared through peer-reviewed publication.

The Facebook emotional contagion study demands a re-evaluation of prevailing research ethics, design values, and algorithmic powers in massive networked architectures. But the current reaction to this study can only have a chilling effect on this debate by removing a unique form responsible disclosure through academic collaboration and publishing. This study is guaranteed to serve as an important case study in the professionalization of data science. But academic researchers should make sure their reactions do not unintentionally inoculate industry against the values and perspectives of social inquiry.

tl;dr:

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