New paper | Critical Companionship: Some Sensibilities for Studying the Lived Experience of Data Subjects

How to study the experiences of people who are being scored by automated systems—and who may not necessarily be aware of their predicament? In this new article entitled Critical Companionship: Some Sensibilities for Studying the Lived Experience of Data Subjects, Malte Ziewitz and Ranjit Singh (PhD ’20) discuss the methodological challenges of studying data subjects over time, drawing on materials from a yearlong investigation of people’s credit score repair practices in Upstate New York.

What are the challenges of turning data subjects into research participants—and how can we approach this task responsibly? In this paper, we develop a methodology for studying the lived experiences of people who are subject to automated scoring systems. Unlike most media technologies, automated scoring systems are designed to track and rate specific qualities of people without their active participation. Credit scoring, risk assessments, and predictive policing all operate obliquely in the background long before they come to matter. In doing so, they constitute a problem not only for those subject to these systems but also for researchers who try to study their experience. Specifically, we identify three challenges that are distinct to studying experiences of automated scoring: limited awareness, embeddedness, and ongoing inquiry. Starting from the observation that coming to terms with one’s position as a data subject constitutes a form of learning in its own right, we propose a research strategy called critical companionship. Originally articulated in the context of nursing research, critical companionship invites us to accompany a data subject over time, paying critical attention to how the participant’s and the researcher’s inquiries complicate and constitute each other. We illustrate the strengths and limitations of this methodology with materials from a recent study we conducted about people’s credit repair practices and sketch a set of sensibilities for studying contemporary scoring systems from the margins.

The article is open access and can be downloaded at Big Data & Society.

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