Ethical Issues in Democratizing Digital Phenotypes and Machine Learning in the Next Generation of Digital Health Technologies

Maurice Mulvenna, RR Bond, Jack Delaney, Fatema Mustansir Dawoodbhoy, Jennifer Boger, Courtney Potts, Robin Turkington

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
171 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1945–1960
Number of pages16
JournalPhilosophy & Technology
Early online date21 Mar 2021
Publication statusPublished (in print/issue) - 31 Dec 2021

Bibliographical note

Publisher Copyright:
© 2021, The Author(s).


  • Ethics
  • Digital health
  • Ecological momentary assessment
  • Experience sampling method
  • Unsupervised machine learning
  • Digital phenotyping
  • Event log analysis


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