Ethical Issues in Democratizing Digital Phenotypes and Machine Learning in the Next Generation of Digital Health Technologies.
Digital health
Digital phenotyping
Ecological momentary assessment
Ethics
Event log analysis
Experience sampling method
Unsupervised machine learning
Journal
Philosophy & technology
ISSN: 2210-5433
Titre abrégé: Philos Technol
Pays: Netherlands
ID NLM: 101583724
Informations de publication
Date de publication:
2021
2021
Historique:
received:
06
07
2020
accepted:
16
02
2021
pubmed:
30
3
2021
medline:
30
3
2021
entrez:
29
3
2021
Statut:
ppublish
Résumé
Digital phenotyping is the term given to the capturing and use of user log data from health and wellbeing technologies used in apps and cloud-based services. This paper explores ethical issues in making use of digital phenotype data in the arena of digital health interventions. Products and services based on digital wellbeing technologies typically include mobile device apps as well as browser-based apps to a lesser extent, and can include telephony-based services, text-based chatbots, and voice-activated chatbots. Many of these digital products and services are simultaneously available across many channels in order to maximize availability for users. Digital wellbeing technologies offer useful methods for real-time data capture of the interactions of users with the products and services. It is possible to design what data are recorded, how and where it may be stored, and, crucially, how it can be analyzed to reveal individual or collective usage patterns. The paper also examines digital phenotyping workflows, before enumerating the ethical concerns pertaining to different types of digital phenotype data, highlighting ethical considerations for collection, storage, and use of the data. A case study of a digital health app is used to illustrate the ethical issues. The case study explores the issues from a perspective of data prospecting and subsequent machine learning. The ethical use of machine learning and artificial intelligence on digital phenotype data and the broader issues in democratizing machine learning and artificial intelligence for digital phenotype data are then explored in detail.
Identifiants
pubmed: 33777664
doi: 10.1007/s13347-021-00445-8
pii: 445
pmc: PMC7981596
doi:
Types de publication
Editorial
Langues
eng
Pagination
1945-1960Informations de copyright
© The Author(s) 2021.
Déclaration de conflit d'intérêts
Conflict of InterestThe authors declare no competing interests.
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