Critical companionship: Some sensibilities for studying the lived experience of data subjects

Abstract

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.

From Fitting Participation to Forging Relationships: The Art of Participatory ML

“…However, a person does not need to be a direct user of a data-driven system to be a data subject. Many data subjects are tracked, scored, and analysed indirectly by data-driven systems-e.g., subjects of algorithmic credit scoring systems [80] informs institutional decisions [78]. These frameworks address concerns that participation alone cannot redistribute power and, without attention, may be more instrumental for researchers than beneficial for participants, reflecting longstanding discussions in PD about other computing systems [14,55,75].…”

Studying Lived Experience and Automated Systems: The Case of Universal Credit

“…In her in‐depth ethnography, Virginia Eubanks (2018) intimately details the personal toll of those subject to automated decision‐making in social security, childcare and housing benefits systems in the U.S. Other studies in this vein examine people's situated, mundane interactions with algorithmic systems, such as users talking to Amazon's Alexa (Strengers & Kennedy, 2022) and news teams maintaining a news ranking algorithm (Svensson, 2022). Ziewitz and Singh (2021) use their study on credit scoring to highlight both the potential and difficulties of studying the lived experiences of data subjects who interact with opaque algorithmic systems.…”

Digitizing Valuation

“…Cochoy 2008), i.e., how the digitization of valuation re-forms spaces and collective agencies that give certain actors more power than others? We might also ask how actors value different configurations of agency (Lee and Helgesson 2020), or how the actors we engage with study, analyze, and think about what a good set-up of agency would be (Ziewitz 2019;Ziewitz and Singh 2021). What new modes of intervention are enabled by digitizing valuation?…”

“…How are relations of accountability reconfigured, and who or what becomes accountable to whom (Ziewitz 2012)? Often it is those people or objects that are measured that are being implicated in accountability webs while the people who construct the measurements of valuation are not (Ziewitz and Singh 2021). How might agencies and infrastructures be reconfigured so that there are possibilities for recourse?…”