Dr Jeffrey Skopek
"Privacy in Numbers? Biological Bodies of Data, Big Data's Epistemology, and the Legal and Ethical Status of Health Inferences”
Conference on Big Data, Health Law, and Bioethics
Harvard Law School
6 May 2016
It is often suggested that big data threatens privacy, and that this is especially true in the health context, where all data may soon be health data. I contend that this common view is mistaken—and that just as we might find truth in numbers, we might find privacy there also. This analysis consists of three main parts. First, I argue that big data will bring about an epistemological shift in the nature of scientific knowledge and inquiry, but not in the ways that are often suggested: rejecting the “end of theory” and “correlation supersedes causation” claims that are often made in the literature, I argue that the real change will be a shift in focus from explanation (and mechanistic causation) to prediction (and difference-making causation). Second, I identify how this shift will transform the ways in which individuals and their medical traits are known: in biobank research, for example, the research subject will no longer be translated into a body of data that is disaggregated, studied, and understood, but rather into a body of data that will, in an important dimension, remain opaque to the researcher. Third, I explore the implications of these developments for privacy, arguing that predictive health algorithms will often not generate the type of justified true beliefs about individuals that are necessary to cause privacy losses—and that even when they do, the predictions will not constitute privacy violations, as they will be mere inferences, which are not the type of epistemic pathway that can violate privacy norms.
Conference webpage:
http://petrieflom.law.harvard.edu/events/details/2016-annual-conference