This fascinating open access paper that was published at the end of August 2021 reflects upon the tension between big data research and the ethical principles that guide the ethical design, conduct and review of human research (looking from within the frame of research in Australia). There is a particular discussion about the tension between biomedical thinking and research practises in areas such as the social sciences. We have included links to 16 related readings.
In this paper, we discuss several problems with current Big data practices which, we claim, seriously erode the role of informed consent as it pertains to the use of personal information. To illustrate these problems, we consider how the notion of informed consent has been understood and operationalised in the ethical regulation of biomedical research (and medical practices, more broadly) and compare this with current Big data practices. We do so by first discussing three types of problems that can impede informed consent with respect to Big data use. First, we discuss the transparency (or explanation) problem. Second, we discuss the re-repurposed data problem. Third, we discuss the meaningful alternatives problem. In the final section of the paper, we suggest some solutions to these problems. In particular, we propose that the use of personal data for commercial and administrative objectives could be subject to a ‘soft governance’ ethical regulation, akin to the way that all projects involving human participants (e.g., social science projects, human medical data and tissue use) are regulated in Australia through the Human Research Ethics Committees (HRECs). We also consider alternatives to the standard consent forms, and privacy policies, that could make use of some of the latest research focussed on the usability of pictorial legal contracts.
Keywords
Big data, AI, Privacy, Informed consent, Moral responsibility
Andreotta, A.J., Kirkham, N. & Rizzi, M. (2021) AI, big data, and the future of consent. AI & Society. https://doi.org/10.1007/s00146-021-01262-5
Publisher (Open Access): https://link.springer.com/article/10.1007/s00146-021-01262-5
