The Challenges of Big Data for Research Ethics Committees: A Qualitative Swiss Study

Abstract

Big data trends in health research challenge the oversight mechanism of the Research Ethics Committees (RECs). The traditional standards of research quality and the mandate of RECs illuminate deficits in facing the computational complexity, methodological novelty, and limited auditability of these approaches. To better understand the challenges facing RECs, we explored the perspectives and attitudes of the members of the seven Swiss Cantonal RECs via semi-structured qualitative interviews. Our interviews reveal limited experience among REC members with the review of big data research, insufficient expertise in data science, and uncertainty about how to mitigate big data research risks. Nonetheless, RECs could strengthen their oversight by training in data science and big data ethics, complementing their role with external experts and ad hoc boards, and introducing precise shared practices.

A Dataset of Lost Opportunities. The Case of 63 Developed and Emerging Countries Amid the Covid-19 Pandemic

“…The article contributes to the literature by developing the theory of the ethical use of information. In contrast to the existing literature (Emmanuel et al [20], Ferretti et al [21,22], Grybauskas et al [23], Jantavongso and Fusiripong [24], Ngan and Kelmenson [25], Novak and Pavlicek [ 26], Reps et al [27],…”

“…[20], Ferretti et al [21,22], Grybauskas et al [23], Jantavongso and Fusiripong [24], Ngan and Kelmenson [25], Novak and Pavlicek [26], Reps et al [27], Robinson [28], Roche and Jamal [29], Seliya et al [thirty].…”

The evolving role of research ethics committees in the era of open data

“…The role of RECs in the transfer of personal information goes beyond legal compliance and should consider individual participant, community and social concerns which may not be explicitly outlined in legal provisions. [17] Also of concern to RECs is the ethical management of research data, including big data (which is according to Ferretti et al [18] computational techniques') or large volumes of data, [19] mainly because traditional ethics oversight procedures and practices, specifically in the field of biomedical and health research, [20] may no longer be adequate. [21] Artificial intelligence (AI) and computational methods to analyse and harness data have played a more prominent role during COVID-19 in the improvement of individual and public health.…”

Big Data in Laboratory Medicine—FAIR Quality for AI?

“…Big Data research proposes novel ethical concerns [ 37 ], mostly surrounding the notions of privacy (hindrance of individual reidentification) and consent (possibility to later revoke consent), where traditional ethics oversight practice is often unaware of the direct societal impact of their decisions [ 38 ]. A recent study in Switzerland showed that members of the seven Swiss RECs had broadly differing views regarding the opportunities and challenges of Big Data, citing insufficient expertise in big data analytics or computer science, to adequately judge the use of Big Data in clinical research [ 39 ]. This situation can become especially cumbersome for researchers when data from different institutions are merged—in this case, modern systems that work with secure multiparty computing and homomorphic encryption, such as the MedCo system, can be a promising approach [ 40 ].…”