Ethical Issues in Social Science Research Employing Big Data

• Published in: Science and engineering ethics Vol. 28; no. 3; p. 29
• Main Authors: Hosseini, Mohammad, Wieczorek, Michał, Gordijn, Bert
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.06.2022; Springer Nature B.V
• Subjects: Big Data; Biomedical Engineering and Bioengineering; Education; Engineering; Ethics; Ethics, Research; Humans; Medicine/Public Health; Original Research/Scholarship; Philosophy; Philosophy of Science; Prejudice; Research ethics; Social Responsibility; Social Sciences; Research Ethics; Social Science; Open Science; Research Integrity; Big Data; Computational Social Science
• ISSN: 1353-3452; 1471-5546; 1471-5546
• DOI: 10.1007/s11948-022-00380-7

This paper analyzes the ethics of social science research (SSR) employing big data. We begin by highlighting the research gap found on the intersection between big data ethics, SSR and research ethics. We then discuss three aspects of big data SSR which make it warrant special attention from a research ethics angle: (1) the interpretative character of both SSR and big data, (2) complexities of anticipating and managing risks in publication and reuse of big data SSR, and (3) the paucity of regulatory oversight and ethical recommendations on protecting individual subjects as well as societies when conducting big data SSR. Against this backdrop, we propose using David Resnik’s research ethics framework to analyze some of the most pressing ethical issues of big data SSR. Focusing on the principles of honesty, carefulness, openness, efficiency, respect for subjects, and social responsibility, we discuss three clusters of ethical issues: those related to methodological biases and personal prejudices, those connected to risks arising from data availability and reuse, and those leading to individual and social harms. Finally, we advance considerations to observe in developing future ethical guidelines about big data SSR.