Critical data studies is in its infancy, but it faces a substantial challenge: as the practice of data science surges ahead, we lack a strong and rigorous sense of ethical parameters for scientific research.

The Belmont Report established three basic principles as foundational for biomedical research governance: respect for persons, beneficence and justice. While these principles get the bulk of attention, perhaps the most consequential contribution for fields like data science is the attempt to define the boundary between research and practice. While acknowledging that the distinction is imperfect in many edge cases, the Report states that this need not cause substantial confusion: “the general rule is that if there is any element of research in an activity, that activity should undergo review for the protection of human subjects” (National Commission for the Protection of Human Subjects, 1979). When routine medical practice veers toward untested territory, it becomes necessary to signify that a matter of clinical care has been “made the object of formal research at an early stage” in order to guarantee its safety (National Commission for the Protection of Human Subjects, 1979).

Rather than seeking to exempt entire classes of new and emerging research, we should be establishing more flexible and informed structures of review, both within the academy and in industry.

In the context of data science, it simply does not hold. Instead, large-scale data practices begin with the assumption that new insights—some extremely sensitive—can be generated through connecting previously disparate data sets. Thus, the Common Rule needs to reflect that even anonymous, public data sets can produce harms depending on how they are used

While we largely agree that A/B testing is a loose fit for existing research ethics regulations, we argue that there are significantly different lessons to be drawn from those gaps

f adopted in a manner that does not allow for tracking the evolving risk profiles of data-intensive research, these new regulations could prematurely close off significant questions about data ethics.

here are a variety of reasons why the predecessors of data science—applied mathematics, statistics and computer science—have had little contact with the infrastructures of ethics review.

There are growing discontinuities between the research practices of data science and established tools of research ethics regulation