Emotional AI is an emerging technology used to make probabilistic predictions about the emotional states of people using data sources, such as facial (micro)-movements, body language, vocal tone or the choice of words. The performance of such systems is heavily debated and so are the underlying scientific methods that serve as the basis for many such technologies. In this article I will engage with this new technology, and with the debates and literature that surround it. Working at the intersection of criminology, policing, surveillance and the study of emotional AI this paper explores and offers a framework of understanding the various issues that these technologies present particularly to liberal democracies. I argue that these technologies should not be deployed within public spaces because there is only a very weak evidence-base as to their effectiveness in a policing and security context, and even more importantly represent a major intrusion to people’s private lives and also represent a worrying extension of policing power because of the possibility that intentions and attitudes may be inferred. Further to this, the danger in the use of such invasive surveillance for the purpose of policing and crime prevention in urban spaces is that it potentially leads to a highly regulated and control-oriented society. I argue that emotion recognition has severe impacts on the right to the city by not only undertaking surveillance of existing situations but also making inferences and probabilistic predictions about future events as well as emotions and intentions.
In this paper, we develop the concept of smart home devices as ‘invisible witnesses’ in everyday life. We explore contemporary examples that highlight how smart devices have been used by the police and unpack the socio-technical implications of using these devices in criminal investigations. We draw on several sociological, computing and forensics concepts to develop our argument. We consider the challenges of obtaining and interpreting trace evidence from smart devices; unpack the ways in which these devices are designed to be ‘invisible in use’; and consider the processes by which they become domesticated into everyday life. We also analyse the differentiated levels of control occupants have over home devices, and the surveillance impacts of making everyday life visible to third parties, particularly the police.
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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.
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