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)

Crowdsourcing platforms are powerful tools for academic researchers. Proponents claim that crowdsourcing helps researchers quickly and affordably recruit enough human subjects with diverse backgrounds to generate significant statistical power, while critics raise concerns about unreliable data quality, labor exploitation, and unequal power dynamics between researchers and workers. We examine these concerns along three dimensions: methods, fairness, and politics. We find that researchers offer vastly different compensation rates for crowdsourced tasks, and address potential concerns about data validity by using platform-specific tools (...) and user verification methods. Additionally, workers depend upon crowdsourcing platforms for a significant portion of their income, are motivated more by fear of losing access to work than by specific compensation rates, and are frustrated by a lack of transparency and occasional unfair treatment from job requesters. Finally, we discuss critical computing scholars’ proposals to address crowdsourcing’s problems, challenges with implementing these resolutions, and potential avenues for future research. (shrink)