The role of an ethics advisory committee in data science research in sub-Saharan Africa

Abstract

Data science research involves large volumes of data, often derived from unconventional sources.Given the complex nature of big data research, there is a strong need for the development of ethically appropriate protocols that are sensitive to the complexities of data science and data sources.While reviews of health research by research ethics committees are necessary from an ethical and legal perspective, complementary advisory committees such as ethics advisory committees could be established to advise on ethics challenges more broadly.In this Perspective, we describe a multidisciplinary ethics advisory committee linked to a data science research hub in sub-Saharan Africa.Data science is an interdisciplinary field in which scientific methods, processes, extremely large data sets, machine learning algorithms and information systems are used to extract knowledge and insights from structured and unstructured data. 1,2The United States National Institutes of Health (NIH)-funded programme, 'Harnessing Data Science for Health Discovery and Innovation in Africa (DS-I Africa)', was established to create a data science research and training network across Africa. 3The Research for Ethical Data Science in sub-Saharan Africa (REDSSA) project, also NIH-funded, is a unique project planned to complement the focus on data science and its emergence in Africa by exploring the ethical, legal, and social implications (ELSI) of this rapidly growing field.Two of the specific goals of the REDSSA ELSI project are to establish a consortium of sub-Saharan African bioethicists "to develop contextualised guidance in the ELSI of data science" and to establish a Data Science Ethics Advisory Committee (EAC) for the research hubs. 2 The REDSSA project is linked to the INFORM-Africa research hub, operating out of Nigeria.This research hub studies the interaction between SARS-CoV-2 and Human Immunodeficiency Virus (HIV) with the goal of using the data to improve pandemic preparedness. 3

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