AI decision-support: a dystopian future of machine paternalism?

Luxton, David D.

doi:10.1136/medethics-2022-108243

Cited by 4 publications

(4 citation statements)

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“…They also involve the education and training of mental health professionals. This includes not only equipping them with the necessary computational skills, but also fostering an understanding of the potential benefits and limitations of computational approaches [19-21] .…”

Section: Resultsmentioning

confidence: 99%

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Current status, challenges and future prospects in computational psychiatry: a narrative review

Vasilchenko,

Chumakov

2023

Consortium Psychiatricum

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BACKGROUND: Computational psychiatry is an area of scientific knowledge which lies at the intersection of neuroscience,psychiatry, and computer science. It employs mathematical models and computational simulations to shed light onthe complexities inherent to mental disorders. AIM: The aim of this narrative review is to offer insight into the current landscape of computational psychiatry, todiscuss its significant challenges, as well as the potential opportunities for the fields growth. METHODS: The authors have carried out a narrative review of the scientific literature published on the topic ofcomputational psychiatry. The literature search was performed in the PubMed, eLibrary, PsycINFO, and Google Scholardatabases. A descriptive analysis was used to summarize the published information on the theoretical and practicalaspects of computational psychiatry. RESULTS: The article relates the development of the scientific approach in computational psychiatry since the mid-1980s.The data on the practical application of computational psychiatry in modeling psychiatric disorders and explaining themechanisms of how psychopathological symptomatology develops (in schizophrenia, attention-deficit/hyperactivitydisorder, autism spectrum disorder, anxiety disorders, obsessive-compulsive disorder, substance use disorders) aresummarized. Challenges, limitations, and the prospects of computational psychiatry are discussed.CONCLUSION: The capacity of current computational technologies in psychiatry has reached a stage where its integrationinto psychiatric practice is not just feasible but urgently needed. The hurdles that now need to be addressed are nolonger rooted in technological advancement, but in ethics, education, and understanding.

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Section: Resultsmentioning

confidence: 99%

“…The field also faces challenges in terms of stigma and social adaptation, particularly among patients with first-episode schizophrenia [19] . The introduction of artificial companions to older adults with cognitive impairment, for example, may raise some concerns.…”

Section: Resultsmentioning

confidence: 99%

Current status, challenges and future prospects in computational psychiatry: a narrative review

Vasilchenko,

Chumakov

2023

Consortium Psychiatricum

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“…Rather, DP will be a part of clinical decision support systems, providing reasonably accurate and reliable outputs that must be received, assessed, and utilised by a human clinician in their diagnostic and treatment processes. Indeed, as with other areas, the perils of automation bias 5 , excessive dependence on AI and AI paternalism (Luxton, 2022) need to be avoided or managed. Furthermore, incorporation of client testimony and 'a commitment to epistemic humility can help promote judicious clinical decision-making at the interface of big data and AI in psychiatry' (McCradden et al, 2023).…”

Section: The Applicability and Utility Of Digital Phenotypingmentioning

confidence: 99%

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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“…AI paternalism, or machine paternalism, is a newly described term that refers to independent decision-making by AI with no, or minimal patient participation. AI paternalism has been previously described in the development of health apps,3 and in AI-assisted clinical diagnosis 4. In contrast, the stakes are much higher in goals of care decision-making in critically ill patients.…”

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confidence: 99%