Empowerment or Engagement? Digital Health Technologies for Mental Healthcare

Burr, Christopher; Morley, Jessica

doi:10.1007/978-3-030-29145-7_5

Cited by 12 publications

(5 citation statements)

References 71 publications

(32 reference statements)

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“…In this context health technologies designed to "empower" (i.e. support human autonomy) create scenarios of control through which potentially shaming or 'victim blaming' messaging fosters introjected motivation, whereby self-worth is contingent on performing the prescribed behaviors (see also Burr and Morley 2019). We argue that a new conceptual lens is needed to make sense of scenarios like these-a lens that considers the different levels at which personal autonomy can be impacted.…”

Section: Autonomy In Context: the Example Of The Youtube Recommender Systemmentioning

confidence: 99%

“…Specifically, YouTube supports uploading of videos, "liking" content, searching, browsing, and creating channels, as well as tasks effected by the recommender system described previously. One example of ethical enquiry at this level is provided by Burr et al (2018) who review the different ways Intelligent Software Agents (ISA), such as recommender systems, interact to achieve their goals. Specifically, they identify four strategies: coercion, deception, trading and nudging provide task level examples such as: "recommending a video or news item, suggesting an exercise in a tutoring task, displaying a set of products and prices".…”

Section: Tasksmentioning

confidence: 99%

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Supporting Human Autonomy in AI Systems: A Framework for Ethical Enquiry

Calvo

Peters

Vold

et al. 2020

Philosophical Studies Series

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Autonomy has been central to moral and political philosophy for millennia, and has been positioned as a critical aspect of both justice and wellbeing. Research in psychology supports this position, providing empirical evidence that autonomy is critical to motivation, personal growth and psychological wellness. Responsible AI will require an understanding of, and ability to effectively design for, human autonomy (rather than just machine autonomy) if it is to genuinely benefit humanity. Yet the effects on human autonomy of digital experiences are neither straightforward nor consistent, and are complicated by commercial interests and tensions around compulsive overuse. This multi-layered reality requires an analysis that is itself multidimensional and that takes into account human experience at various levels of resolution. We borrow from HCI and psychological research to apply a model (“METUX”) that identifies six distinct spheres of technology experience. We demonstrate the value of the model for understanding human autonomy in a technology ethics context at multiple levels by applying it to the real-world case study of an AI-enhanced video recommender system. In the process we argue for the following three claims: (1) There are autonomy-related consequences to algorithms representing the interests of third parties, and they are not impartial and rational extensions of the self, as is often perceived; (2) Designing for autonomy is an ethical imperative critical to the future design of responsible AI; and (3) Autonomy-support must be analysed from at least six spheres of experience in order to appropriately capture contradictory and downstream effects.

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Section: Autonomy In Context: the Example Of The Youtube Recommender Systemmentioning

confidence: 99%

Section: Tasksmentioning

confidence: 99%

Supporting Human Autonomy in AI Systems: A Framework for Ethical Enquiry

Calvo

Peters

Vold

et al. 2020

Philosophical Studies Series

View full text Add to dashboard Cite

show abstract

“…In the discourse on digital (mental) health, control brings up the related term empowerment and the perspective that technology which facilitates an information sharing/collecting process empowers the client in their care. This perspective has been criticised for placing excessive responsibility on the individual to take care of themselves, whilst disenfranchising them from the support of professionals and institutions who in turn relinquish responsibility for the individual (Burr et al, 2020;Lovatt & Holmes, 2017). In the case proposed here, a person-centred DP system's effectiveness depends not only upon client control, but also on the careful guidance of the clinician and the forging of a feedback loop between the two-a strong therapeutic alliance.…”

Section: Awareness Empowerment and Strengthening The Therapeutic Alli...mentioning

confidence: 97%

Ethical Dimensions of Digital Phenotyping Within the Context of Mental Healthcare

D’Alfonso,

Coghlan,

Schmidt

et al. 2024

J. technol. behav. sci.

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Digital phenotyping (DP) refers to the emerging field within digital (mental) health that involves the collection of data from individual’s digital devices (smartphones, wearable, Internet usage, etc.) to monitor and analyse their behaviours, activities and health-related patterns. That such ‘digital footprint’ data can be mined for behavioural insights is an intriguing idea, which has motivated an increasing amount of research activity, particularly in the field of digital mental health. Whilst of potentially revolutionary utility in (mental) healthcare, the idea of DP also raises a set of rich sociotechnical, ethical and philosophical considerations, and a cottage industry of sociotechnical and ethical critiques of DP has emerged, particularly within the humanities. Within this scene of early empirical investigation in the health/behavioural sciences on the one hand and general conceptual critiques from the humanities on the other, in this paper we instead explore a case for the potential utility of DP in circumstances of clinical mental health practice and examine its ethical dimensions in this context. After providing an explicatory framework for DP and making a case for it within this framework, we subsequently examine the ethical pros and cons of three topics pertaining to DP in clinical practice, namely (1) issues in psychometric assessment/testing, (2) the role and responsibilities of mental health practitioners with regard to DP technology, and (3) the value DP can afford clients in terms of self-awareness/empowerment and strengthening the therapeutic alliance with their clinician.

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“…Third, these logics can have the effect of responsibilizing health care, pushing monitoring and management further into the domain of individual patients and caregivers (Rich et al, 2019), often through design decisions that nudge health-related behaviours. While frequently lauded for their potential to more effectively engage patients in their own care, these perspectives have been critiqued for oversimplifying the meaning and value of engagement in digital health (Burr and Morley, 2020). Some, for example, point to the unrecognized ‘repair work’ that often accompanies the use of digital health technologies (Forlano, 2020; Schwennesen, 2019).…”

Section: Pitfalls Associated With Co-design and Ethical Ai/ml For Healthmentioning

confidence: 99%

Co-design and ethical artificial intelligence for health: An agenda for critical research and practice

Donia

Shaw

2021

Big Data & Society

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Applications of artificial intelligence/machine learning (AI/ML) in health care are dynamic and rapidly growing. One strategy for anticipating and addressing ethical challenges related to AI/ML for health care is patient and public involvement in the design of those technologies – often referred to as ‘co-design’. Co-design has a diverse intellectual and practical history, however, and has been conceptualized in many different ways. Moreover, AI/ML introduces challenges to co-design that are often underappreciated. Informed by perspectives from critical data studies and critical digital health studies, we review the research literature on involvement in health care, and involvement in design, and examine the extent to which co-design as commonly conceptualized is capable of addressing the range of normative issues raised by AI/ML for health care. We suggest that AI/ML technologies have amplified and modified existing challenges related to patient and public involvement, and created entirely new challenges. We outline three pitfalls associated with co-design for ethical AI/ML for health care and conclude with suggestions for addressing these practical and conceptual challenges.

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Empowerment or Engagement? Digital Health Technologies for Mental Healthcare

Cited by 12 publications

References 71 publications

Supporting Human Autonomy in AI Systems: A Framework for Ethical Enquiry

Supporting Human Autonomy in AI Systems: A Framework for Ethical Enquiry

Ethical Dimensions of Digital Phenotyping Within the Context of Mental Healthcare

Co-design and ethical artificial intelligence for health: An agenda for critical research and practice

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