This article explores the socio-algorithmic construction of identity categories based on an ethnographic study of the Israeli data analytics industry. While algorithmic categorization has been described as a post-textual phenomenon that leaves language, social theory, and social expertise behind, this article focuses on the return of the social—the process through which the symbolic means resurface to turn algorithmically produced clusters into identity categories. I show that such categories stem not only from algorithms’ structure or their data, but from the social contexts from which they arise, and from the values assigned to them by various individuals. I accordingly argue that algorithmic identities stem from epistemic amalgams—complex blends of algorithmic outputs and human expertise, messy data flows, and diverse inter-personal factors. Finally, I show that this process of amalgamation arbitrarily conjoins quantitative clusters with qualitative labels, and I discuss the implausibility of seeing named algorithmic categories as explainable.
Bibliographical notePublisher Copyright:
© The Author(s) 2020.
- Algorithmic identity
- data analytics
- explainable algorithms
- online profiling
ASJC Scopus subject areas
- Sociology and Political Science