Abstract
This chapter serves as an introduction to the edited collection of the same name, which includes chapters that explore digital well-being from a range of disciplinary perspectives, including philosophy, psychology, economics, health care, and education. The purpose of this introductory chapter is to provide a short primer on the different disciplinary approaches to the study of well-being. To supplement this primer, we also invited key experts from several disciplines—philosophy, psychology, public policy, and health care—to share their thoughts on what they believe are the most important open questions and ethical issues for the multi-disciplinary study of digital well-being. We also introduce and discuss several themes that we believe will be fundamental to the ongoing study of digital well-being: digital gratitude, automated interventions, and sustainable co-well-being.
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- 1.
The most prominent zero-priced goods and services are often digital goods themselves, such as search engines or social media platforms.
- 2.
See (Calvo et al. 2015) for an introduction to current techniques in affective computing.
- 3.
- 4.
To clarify, our use of the term ‘automated decision-making systems’ is intended to be inclusive of systems that are fully automated (i.e. not requiring human oversight) and also decision support tools that keep a human-in-the-loop. Furthermore, we treat the act of classification as a decision (e.g. the classification of disease on the basis of a radiology image).
- 5.
This example is based on the work of a research group at MIT’s Media Lab, who have developed a product (AttentivU) that seeks to improve attention through real-time monitoring of a user’s engagement, using a head-mounted device that use physiological sensors (i.e. electroencephalography) to measure engagement (Kosmyna et al. 2019).
- 6.
- 7.
Technically, this is known as ‘collaborative filtering’, which is a method for using the collaborative actions of users (e.g., which videos they watch, how long they watch them for, and what rating they give them) as ‘implicit feedback’ to train a recommender system (see Burr et al. 2018; Milano et al. 2020 for further discussion).
- 8.
This point is clearly demonstrated by the widespread adoption of well-being in recent frameworks or guidelines for ethical technology design, including AI (see Floridi et al. 2018).
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Burr, C., Floridi, L. (2020). The Ethics of Digital Well-Being: A Multidisciplinary Perspective. In: Burr, C., Floridi, L. (eds) Ethics of Digital Well-Being. Philosophical Studies Series, vol 140. Springer, Cham. https://doi.org/10.1007/978-3-030-50585-1_1
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