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The ethnographer and the algorithm: beyond the black box

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Abstract

A common theme in social science studies of algorithms is that they are profoundly opaque and function as “black boxes.” Scholars have developed several methodological approaches in order to address algorithmic opacity. Here I argue that we can explicitly enroll algorithms in ethnographic research, which can shed light on unexpected aspects of algorithmic systems—including their opacity. I delineate three meso-level strategies for algorithmic ethnography. The first, algorithmic refraction, examines the reconfigurations that take place when computational software, people, and institutions interact. The second strategy, algorithmic comparison, relies on a similarity-and-difference approach to identify the instruments’ unique features. The third strategy, algorithmic triangulation, enrolls algorithms to help gather rich qualitative data. I conclude by discussing the implications of this toolkit for the study of algorithms and future of ethnographic fieldwork.

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Notes

  1. While there are many variations on what people mean by ethnography, ethnographers across disciplines typically agree on several points. Epistemologically, many ethnographers rely on some version of “grounded theory” (Glaser and Strauss 1967, but see also Burawoy 1998, Timmermans and Tavory 2012 for different approaches), starting with a preliminary research question that evolves based on the data collected during fieldwork. Theoretically, ethnographic methods share multiple affinities with symbolic interactionism, which understands individual interactions as a key building block of social life (Mead 1967; Blumer 1969; Goffman 1959). In terms of methods, ethnography often involves participant observation, in which observers actively engage in the activities of the people they study.

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Acknowledgments

I would like to thank Sharon Zukin, John Torpey, Fred Turner, Melissa Valentine, Rebecca Hinds, and the participants of the Center on Digital Culture and Society Launch Symposium (University of Pennsylvania, Annenberg School of Communication), the Médialab (Sciences-Po)/Centre Internet et Société (CNRS) seminar, and the Communication Works in Progress (CWIP) workshop (Stanford University) for their feedback on previous versions of this article. This research was supported by the Chair “Major Social Changes” (Sorbonne Université—Institut d’Etudes Avancées de Paris).

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    Angèle Christin

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Christin, A. The ethnographer and the algorithm: beyond the black box. Theor Soc 49, 897–918 (2020). https://doi.org/10.1007/s11186-020-09411-3

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