Self-quantification and the datapreneurial consumer identity

DuFault, Beth; Schouten, John W.

doi:10.1080/10253866.2018.1519489

Cited by 31 publications

(14 citation statements)

References 53 publications

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“…Starting once again with the individual LoA: as more diagnostic and therapeutic interventions become based on AI-Health solutions, individuals may be encouraged to share more and more personal data about themselves (Racine et al, 2019) -data that can then be used in opaque ways (Sterckx et al, 2016). This means that the ability for individuals to be meaningfully involved in shared decision making is considerably undermined As a result, the increasing use of algorithmic decision-making in clinical settings can have negative implications for individual autonomy, as for an individual to be able to exert agency over the AI-Health derived clinical decision, they would need to have a good understanding of the underlying data, processes and technical possibilities that were involved in it being reached (DuFault & Schouten, 2018) and be able to ensure their own values are taken into consideration (McDougall, 2019). The vast majority of the population do not have the level of eHealth literacy necessary for this (Kim & Xie, 2017), and those that do (including HCPs) are prevented from gaining this understanding due to the black-box nature of AI-Health algorithms (Watson et al, 2019).…”

Section: Normative Concerns: Unfair Outcomes and Transformative Effectsmentioning

confidence: 99%

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

Morley¹

2019

Preprint

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Healthcare systems across the globe are struggling with increasing costs and worsening outcomes. This presents those responsible for overseeing healthcare with a challenge. Increasingly, policymakers, politicians, clinical entrepreneurs and computer and data scientists argue that a key part of the solution will be ‘Artificial Intelligence’ (AI) – particularly Machine Learning (ML). This argument stems not from the belief that all healthcare needs will soon be taken care of by “robot doctors.” Instead, it is an argument that rests on the classic counterfactual definition of AI as an umbrella term for a range of techniques that can be used to make machines complete tasks in a way that would be considered intelligent were they to be completed by a human. Automation of this nature could offer great opportunities for the improvement of healthcare services and ultimately patients’ health by significantly improving human clinical capabilities in diagnosis, drug discovery, epidemiology, personalised medicine, and operational efficiency. However, if these AI solutions are to be embedded in clinical practice, then at least three issues need to be considered: the technical possibilities and limitations; the ethical, regulatory and legal framework; and the governance framework. In this article, we report on the results of a systematic analysis designed to provide a clear overview of the second of these elements: the ethical, regulatory and legal framework. We find that ethical issues arise at six levels of abstraction (individual, interpersonal, group, institutional, sectoral, and societal) and can be categorised as epistemic, normative, or overarching. We conclude by stressing how important it is that the ethical challenges raised by implementing AI in healthcare settings are tackled proactively rather than reactively and map the key considerations for policymakers to each of the ethical concerns highlighted.

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Section: Normative Concerns: Unfair Outcomes and Transformative Effectsmentioning

confidence: 99%

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

Morley¹

2019

Preprint

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“…The mood of incompletion that the behavioural surplus project engenders is perhaps best evidenced by previous research that focuses on individuals or groups who self-elect to generate and curate data from their day-to-day activities – a phenomenon that has been referred to as ‘dataist’ lifestyles (DuFault and Schouten, 2020), ‘self-tracking’ (Charitsis et al, 2019), ‘everyday analytics’ (Pantzar and Ruckenstein, 2015), ‘lived informatics’ (Rooksby et al, 2014) or ‘lifelogging’ (Räikkönen and Grénman, 2020). For example, in prior ethnographic engagements with members of the Quantified Self (QS) community – an international collective that shares insights from personal data – we see how self-tracking technologies are welcomed into consumers’ lives to enhance self-knowledge and optimise the self, despite self-trackers’ recognition of surveillance capitalism’s privacy threats (Bode and Kristensen, 2015; Kristensen and Ruckenstein, 2018).…”

Section: Introductionmentioning

confidence: 99%

High-fidelity consumption and the claustropolitan structure of feeling

2021

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This paper invokes Redhead’s concept of claustropolitanism to critically explore the affective reality for consumers in today’s digital age. In the context of surveillance capitalism, we argue that consumer subjectivity revolves around the experience of fidelity rather than agency. Instead of experiencing genuine autonomy in their digital lives, consumers are confronted with a sense of confinement that reflects their tacit conformity to the behavioural predictions of surveillant market actors. By exploring how that confinement is lived and felt, we theorise the collective affects that constitute a claustropolitan structure of feeling: incompletion, saturation and alienation. These affective contours trace an oppressive atmosphere that infuses consumers’ lives as they attempt to seek fulfilment through digital market-located behaviours that are largely anticipated and coordinated by surveillant actors. Rather than motivate resistance, these affects ironically work to perpetuate consumers’ commitment to the digital world and their ongoing participation in the surveillant marketplace. Our theorisation continues the critical project of re-assessing the consumer subject by showing how subjectivity is produced at the point of intersection between ideological imperatives and affective consequences.

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“…In the case of credit scoring, for example, most work to date has relied on statistical analyses of datasets (Fellowes, 2006; Nelson, 2010) or quantitative surveys (Arya et al, 2013; Levinger et al, 2011). More recently, researchers have turned to more interpretive methods, analyzing discourse in discussion forums (Deville, 2016; DuFault and Schouten, 2020; Mackenzie, 2017) or conducting ethnographic studies of marginalized groups and financial inclusion advocates (Kear, 2017). Beyond that, more mundane forms of sense-making, folk theories, and everyday experiences of credit scoring have remained under-explored, even within scholarship on the Quantified Self movement (DuFault and Schouten, 2020).…”

Section: From Data Systems To Data Subjectsmentioning

confidence: 99%

“…Even Facebook users who interact with personalized ads and news feeds on a daily basis are not necessarily aware that they are being algorithmically targeted (Eslami et al, 2015: 153, 2016; Rader and Gray, 2015: 178). In an analysis of online credit forums, Dufault and Schouten (2020: 302) found that it usually takes a “precipitating incident” or negative event to trigger such awareness. The problem, then, is that any study focusing on the experiences of data subjects will inevitably prompt participants with information that is likely to transform their understanding of their own position vis-à-vis the system.…”

Section: Pitfalls For Empirical Inquirymentioning

confidence: 99%

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

Ziewitz

Singh

2021

Big Data & Society

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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.

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Self-quantification and the datapreneurial consumer identity

Cited by 31 publications

References 53 publications

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

High-fidelity consumption and the claustropolitan structure of feeling

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

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