Mental health-related data generated by app users during the routine use of Consumer Mental Health Apps (CMHAs) are being increasingly leveraged for research and product improvement studies. However, it remains unclear which ethical safeguards and practices should be implemented by researchers and app developers to protect users during these studies, and concerns have been raised over their current implementation in CMHAs. To better understand which ethical safeguards and practices are implemented, why and how, 17 app developers and researchers were interviewed (...) who had been involved in using CMHA data for studies. Interviewees discussed the impact on stakeholder interests, sufficiency thresholds and procedural alterations of informed consent, data protection, gathering app user perspectives and representing users in app design and study conduct, and ensuring adequate support. Although the reasoning behind how and why these ethical safeguards and practices should be implemented showed considerable variability and several gaps, interviewees converged on various general lines of reasoning. This allowed for the development of a coherent and nuanced account that could prove useful for future CMHA studies and which could stimulate further normative investigation of the role of ethical safeguards and practices in these studies. (shrink)

This article examines whether a crowdsourced research participant who quits a study before its completion should receive any monetary compensation. The study is focused on participants recruited from Amazon Mechanical Turk, the most widely used crowdsourcing platform, and examines the tensions between participants’ rights and research objectives when online labor markets are used to recruit research participants. The discussion is informed by the recent literature on online research with crowdsourced samples, evidence from human subjects’ practices at top US universities, and (...) an ethical analysis based on distributive justice and consequentialism. The results indicate that compensating crowdsourced research subjects who fail to complete their participation jeopardizes the benefits of many who dutifully participate and undermines the overall value of the research enterprise for advancing knowledge. By examining the issue of payment for crowdsourced participants who drop out, this article sheds light on modern ethical issues in online research and offers practical recommendations for researchers and ethics scholars. (shrink)

A recent systematic review of Machine Learning (ML) approaches to health data, containing over 100 studies, found that the most investigated problem was mental health (Yin et al., 2019). Relatedly, recent estimates suggest that between 165,000 and 325,000 health and wellness apps are now commercially available, with over 10,000 of those designed specifically for mental health (Carlo et al., 2019). In light of these trends, the present chapter has three aims: (1) provide an informative overview of some of the recent (...) work taking place at the intersection of text mining and mental health so that we can (2) highlight and analyze several pressing ethical issues that are arising in this rapidly growing field and (3) suggest productive directions for how these issues might be better addressed within future interdisciplinary work to ensure the responsible development of text mining approaches in psychology generally, and in mental health fields, specifically. In Section 1, we review some of the recent literature on text-mining and mental health in the contexts of traditional experimental settings, social media, and research involving electronic health records. Then, in Section 2, we introduce and discuss ethical concerns that arise before, during, and after research is conducted. Finally, in Section 3, we offer several suggestions about how ethical oversight of text-mining research might be improved to be more responsive to the concerns mapped out in Section 2. (shrink)