“Human-centered machine learning” (HCML) combines human insights and domain expertise with data-driven predictions to answer societal questions. This area’s inherent interdisciplinarity causes tensions in the obligations researchers have to the humans whose data they use. This paper studies how scientific papers represent human research subjects in HCML. Using mental health status prediction on social media as a case study, we conduct thematic discourse analysis on 55 papers to examine these representations. We identify five discourses that weave a complex narrative of who the human subject is in this research: Disorder/Patient, Social Media, Scientific, Data/Machine Learning, and Person. We show how these five discourses create paradoxical subject and object representations of the human, which may inadvertently risk dehumanization. We also discuss the tensions and impacts of interdisciplinary research; the risks of this work to scientific rigor, online communities, and mental health; and guidelines for stronger HCML research in this nascent area.
|Original language||English (US)|
|Journal||Proceedings of the ACM on Human-Computer Interaction|
|State||Published - Nov 2019|
Bibliographical noteFunding Information:
The authors would like to thank Brianna Dym, Michaelanne Dye, Os Keyes, Karen Boyd, and the anonymous reviewers for their feedback. This project was funded in part by NIH Award #R01MH117172 and NSF Award #1816403.
© 2019 Association for Computing Machinery.
- Human-centered machine learning
- Machine learning
- Mental health
- Research ethics
- Social media