Abstract
The emergence of massive datasets collected using automated or large scale data harvesting methodologies ushered in by digital tools creates new challenges for researchers with respect to the ethical use and securing of private information. Such concerns are heightened by the fact that big data is likely to be reused, reanalyzed, recombined, and repurposed without explicit consent from the original data providers. The present chapter offers a methodology for assessing the ethical nature of a given big data use situation. The approach is contextual and relational. Researchers are invited to use a privacy matrix that intersects five ethical concerns (non-maleficence, beneficence, justice, autonomy, and trust) with four possible contexts of use (social, science, government, and science). The matrix can be used to determine the significance of the ethical problem for a given situation by tallying the number and assessing the nature of concerns present in using the data in a given context. Furthermore, the heuristic procedure can be enhanced with a modified type of trade-off analysis which starts from a minimum ethical threshold below which trade-offs should not be performed. The matrix and the heuristic method offer a pragmatic, yet ethically grounded approach to dealing with the complex nature of privacy in the context of big data research.
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Steinmann, M., Matei, S.A., Collmann, J. (2016). A Theoretical Framework for Ethical Reflection in Big Data Research. In: Collmann, J., Matei, S. (eds) Ethical Reasoning in Big Data. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-28422-4_2
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DOI: https://doi.org/10.1007/978-3-319-28422-4_2
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