Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review

Background Uncertainty surrounds the ethical and legal implications of algorithmic data-driven technologies in mental health context, including characterized as artificial intelligence, machine learning, deep other forms automation. Objective This study aims to survey empirical scholarly literature on application initiatives identify issues that have been raised. Methods We searched for peer-reviewed studies care Scopus, Embase, Association Computing Machinery databases. A total 1078 relevant applied were identified, which narrowed 132 research papers review based selection criteria. Conventional content analysis was undertaken address our aims, this supplemented by a keyword-in-context analysis. Results grouped findings into following five categories technology: social media (53/132, 40.1%), smartphones (37/132, 28%), sensing technology (20/132, 15.1%), chatbots (5/132, 3.8%), miscellaneous (17/132, 12.9%). Most directed toward detection diagnosis. discussed privacy, mainly terms respecting privacy participants. There relatively little discussion context. small number ethics directly (10/132, 7.6%) indirectly 7.6%). Legal not substantively any studies, although some passing (7/132, 5.3%), such rights user subjects law compliance. Conclusions Ethical tend be explicitly addressed initiatives. Scholars may considered or matters at committee institutional board stage. If so, consideration seldom appears published materials detail. The form itself detail field well preclude substantial focus law. Regardless, we identified several concerns, near-complete lack involvement service users, scant accountability, potential overmedicalization techno-solutionism. computer science pilot exploratory stages. Thus, these could appropriated practice rarely acknowledged ways, with serious implications.