Ethics and epistemics in biomedical big data research

This recent article explores issues in big data approaches with regard to

a) ethics – such as obtaining consent from large numbers of research participants across a large number of institutions; protect confidentiality; privacy concerns; optimal methods for de-identification; and the limitation of the capacity for the public, and even experts, to interpret and question research findings

and b) epistemics – such as personalized (or precision) treatment that rely on extending concepts that have largely failed or have very high error rates; deficiencies of observational studies that do not get eliminated with big data; challenges of big data approaches due to their overpowered analysis settings; minor noise due to errors or low quality information being easily be translated into false signals; and problems with the view that big data is somehow “objective,” including that this obscures the fact that all research questions, methods, and interpretations
are value-laden.

The article closes with a list of recommendations which consider the tight links between epistemology and ethics in relation to big data in biomedical research.

Wendy Lipworth, Paul H. Mason, Ian Kerridge, John P. A. Ioannidis, Ethics and Epistemology in Big Data Research, in: Journal of Bioethical Inquiry 2017, DOI 10.1007/s11673-017-9771-3 [Epub ahead of print].