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Ethical issues in social science research employing big data

Hosseini, Mohammad orcid logoORCID: 0000-0002-2385-985X, Wieczorek, Michał orcid logoORCID: 0000-0003-3688-9684 and Gordijn, Bert orcid logoORCID: 0000-0002-3686-8659 (2022) Ethical issues in social science research employing big data. Science and Engineering Ethics, 28 (3). ISSN 1353-3452

Abstract
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.
Metadata
Item Type:Article (Published)
Refereed:Yes
Additional Information:Article number: 29
Subjects:Humanities > Philosophy
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Humanities and Social Science > School of Theology, Philosophy, & Music
Publisher:Springer
Official URL:https://dx.doi.org/10.1007/s11948-022-00380-7
Copyright Information:© 2022 The Authors.
Funders:European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 741782, NUCATS, UL1TR001422, European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813,497
ID Code:27318
Deposited On:17 Jun 2022 14:51 by Michal Wieczorek . Last Modified 14 Mar 2023 15:56
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