Emotions and Dynamic Assemblages : A Study of Automated Social Security Using Qualitative Longitudinal Research

Currie, Morgan and Podoletz, Lena (2023) Emotions and Dynamic Assemblages : A Study of Automated Social Security Using Qualitative Longitudinal Research. In: Proceedings of the 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023 :. ACM International Conference Proceeding Series . Association for Computing Machinery (ACM), New York, pp. 1101-1111. ISBN 9781450372527

Abstract

In this paper we argue that qualitative longitudinal research (QLLR) is a crucial research method for studying automated decision-making (ADM) systems as complex, dynamic digital assemblages. QLLR provides invaluable insight into the lived experiences of users as data subjects of ADMs as well as into the broader digital assemblage in which these systems operate. To demonstrate the utility of this method, we draw on an ongoing, empirical study examining Universal Credit (UC), an automated social security payment used in the United Kingdom. UC is digital-by-default and uses a dynamic, means-testing payment system to determine the monthly amount of claim people are entitled to. We first provide a brief overview of the key epistemological challenges of studying ADMs before situating our study in relation to existing qualitative analyses of ADMs and their users, as well as qualitative longitudinal research. We highlight that, thus far, QLLR has been severely under-utilized in studying ADM systems. After a brief description of our study, aims and methodology, we present our findings illustrated through empirical cases that demonstrate the potential of QLLR in this area. Overall, we argue that QLLR provides a unique opportunity to gather information on ADMs, both over time and in real time. Capturing information real-time allows for more granular accounts and provides an opportunity for gathering in situ data on emotions and attitudes of users and data subjects. The ability to record qualitative data over time has the potential to capture dynamic trajectories, including the fluctuations and uncertainties comprising users’ lived experiences. Through the personal accounts of data subjects, QLLR also gives researchers insight into how the emotional dimensions of users’ interactions with ADMs shapes their actions responding to these systems.