Toward a Posthumanist Ethics of Qualitative Research in a Big Data Era

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Abstract

The Big Data phenomenon, and its uptake in qualitative research, raises ethical issues around data aggregation, data linkages, and data anonymization as well as concerns around changing meanings and possibilities of informed consent and privacy protection. In this article, I address the ethical issues that arise from Big Data through a posthumanist philosophical framework. The humanist ethics that underpins normative ethical concerns—as outlined above—focuses on the unequal power relationship between researchers and research subjects and the potential harm that research can cause to research participants. Ethical practice consists in following guidelines and codes of ethical conduct designed, not so much to avoid these power differentials, but to protect research participants from potential exploitation and infringements of their human rights. Unethical research is understood as research that breaches these principles and/or harms its research subjects. A posthumanist ethics treats knowledge-making itself as a matter of ethical concern. It shifts the focus away from the power of researchers over research participants toward the “world-making” powers of practices of inquiry: their ability to constitute (and not simply discover) the very nature of their objects/subjects of study. Its focus of ethical concern—what it regards as unethical—is research that claims to represent the world “as it really is.” On this approach, ethical practice consists in accounting for the ways in which research ontologically constitutes its objects and subjects of study. The critical intervention made possible by bringing a posthumanist perspective to bear on the ethics of qualitative research in a Big Data era is to foreground Big Data’s treatment of data as self-evident, and its positivist claim to represent the world innocently, accurately, and objectively, as matters of ethical concern.

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APA

Mauthner, N. S. (2019). Toward a Posthumanist Ethics of Qualitative Research in a Big Data Era. American Behavioral Scientist, 63(6), 669–698. https://doi.org/10.1177/0002764218792701

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