The Ethics of Digital Well-Being: A Thematic Review

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Abstract

This article presents the first thematic review of the literature on the ethical issues concerning digital well-being. The term ‘digital well-being’ is used to refer to the impact of digital technologies on what it means to live a life that is good for a human being. The review explores the existing literature on the ethics of digital well-being, with the goal of mapping the current debate and identifying open questions for future research. The review identifies major issues related to several key social domains: healthcare, education, governance and social development, and media and entertainment. It also highlights three broader themes: positive computing, personalised human–computer interaction, and autonomy and self-determination. The review argues that three themes will be central to ongoing discussions and research by showing how they can be used to identify open questions related to the ethics of digital well-being.

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Most cited references 51

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The need for a new medical model: a challenge for biomedicine.

Andrew G. Engel (1977)

The dominant model of disease today is biomedical, and it leaves no room within tis framework for the social, psychological, and behavioral dimensions of illness. A biopsychosocial model is proposed that provides a blueprint for research, a framework for teaching, and a design for action in the real world of health care.

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Friend networking sites and their relationship to adolescents' well-being and social self-esteem.

Patti Valkenburg, Jochen Peter, Alexander P. Schouten (2006)

The aim of this study was to investigate the consequences of friend networking sites (e.g., Friendster, MySpace) for adolescents' self-esteem and well-being. We conducted a survey among 881 adolescents (10-19-year-olds) who had an online profile on a Dutch friend networking site. Using structural equation modeling, we found that the frequency with which adolescents used the site had an indirect effect on their social self-esteem and well-being. The use of the friend networking site stimulated the number of relationships formed on the site, the frequency with which adolescents received feedback on their profiles, and the tone (i.e., positive vs. negative) of this feedback. Positive feedback on the profiles enhanced adolescents' social self-esteem and well-being, whereas negative feedback decreased their self-esteem and well-being.

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A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain

Yiming Ding, Jae Sohn, Michael G. Kawczynski … (2018)

Purpose To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (18F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to that of radiologic readers. Materials and Methods Prospective 18F-FDG PET brain images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (2109 imaging studies from 2005 to 2017, 1002 patients) and retrospective independent test set (40 imaging studies from 2006 to 2016, 40 patients) were collected. Final clinical diagnosis at follow-up was recorded. Convolutional neural network of InceptionV3 architecture was trained on 90% of ADNI data set and tested on the remaining 10%, as well as the independent test set, with performance compared to radiologic readers. Model was analyzed with sensitivity, specificity, receiver operating characteristic (ROC), saliency map, and t-distributed stochastic neighbor embedding. Results The algorithm achieved area under the ROC curve of 0.98 (95% confidence interval: 0.94, 1.00) when evaluated on predicting the final clinical diagnosis of AD in the independent test set (82% specificity at 100% sensitivity), an average of 75.8 months prior to the final diagnosis, which in ROC space outperformed reader performance (57% [four of seven] sensitivity, 91% [30 of 33] specificity; P < .05). Saliency map demonstrated attention to known areas of interest but with focus on the entire brain. Conclusion By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Larvie in this issue.

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Author and article information

Contributors

Christopher Burr:

ORCID: http://orcid.org/0000-0003-0386-8182

christopher.burr@oii.ox.ac.uk

Journal

Journal ID (nlm-ta): Sci Eng Ethics

Journal ID (iso-abbrev): Sci Eng Ethics

Title: Science and Engineering Ethics

Publisher: Springer Netherlands (Dordrecht )

ISSN (Print): 1353-3452

ISSN (Electronic): 1471-5546

Publication date (Electronic): 13 January 2020

Publication date PMC-release: 13 January 2020

Publication date (Print): 2020

Volume: 26

Issue: 4

Pages: 2313-2343

Affiliations

[1 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Oxford Internet Institute, , University of Oxford, ; 1 St Giles, Oxford, OX1 3JS UK

[2 ]GRID grid.499548.d, ISNI 0000 0004 5903 3632, The Alan Turing Institute, ; 96 Euston Road, London, NW1 2DB UK

Author information

Christopher Burr http://orcid.org/0000-0003-0386-8182

Mariarosaria Taddeo http://orcid.org/0000-0002-1181-649X

Luciano Floridi http://orcid.org/0000-0002-5444-2280

Article

Publisher ID: 175

DOI: 10.1007/s11948-020-00175-8

PMC ID: 7417400

PubMed ID: 31933119

SO-VID: 7a696543-cc5f-40bf-a987-6cb6c418e37f

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 7 February 2019

Date accepted : 3 January 2020

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100006112, Microsoft Research;

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ScienceOpen disciplines: Ethics

Keywords: artificial intelligence,digital well-being,ethics of technology,positive computing,self-determination

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ScienceOpen disciplines: Ethics

Keywords: artificial intelligence, digital well-being, ethics of technology, positive computing, self-determination

The Ethics of Digital Well-Being: A Thematic Review

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Special feature: AI and the Human in Education

Most cited references 51

The need for a new medical model: a challenge for biomedicine.

Friend networking sites and their relationship to adolescents' well-being and social self-esteem.

A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain

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