Abstract
Social media have become a rich source of data, particularly in health research. Yet, the use of such data raises significant ethical questions about the need for the informed consent of those being studied. Consent mechanisms, if even obtained, are typically broad and inflexible, or place a significant burden on the participant. Machine learning algorithms show much promise for facilitating a “middle-ground” approach: using trained models to predict and automate granular consent decisions. Such techniques, however, raise a myriad of follow-on ethical and technical considerations. In this article, we present an exploratory user study (n = 67) in which we find that we can predict the appropriate flow of health-related social media data with reasonable accuracy, while minimizing undesired data leaks. We then attempt to deconstruct the findings of this study, identifying and discussing a number of real-world implications if such a technique were put into practice.
| Original language | English |
|---|---|
| Pages (from-to) | 187-201 |
| Number of pages | 15 |
| Journal | Journal of Empirical Research on Human Research Ethics |
| Volume | 15 |
| Issue number | 3 |
| Early online date | 6 Nov 2019 |
| DOIs | |
| Publication status | Published - 1 Jul 2020 |
| Externally published | Yes |
Bibliographical note
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Wellcome Trust [UNS19427]. The first author has since benefitted from Microsoft funding through the Microsoft Cloud Computing Research Centre (MCCRC).Keywords
- contextual integrity
- health support networks
- informed consent
- privacy
- social media