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

中文翻译：

---

精神卫生保健算法和数据驱动技术研究中的伦理和法律：范围界定审查

背景：在心理健康领域，算法和数据驱动技术的伦理和法律影响存在不确定性，包括人工智能、机器学习、深度学习和其他形式的自动化技术。目的：本研究旨在调查有关算法和数据驱动技术在心理健康举措中应用的实证学术文献，以确定已提出的法律和伦理问题。方法：我们在 Scopus、Embase 和 Association for Computer Machinery 数据库中检索了有关算法技术在心理健康护理中应用的同行评审实证研究。共确定了 1078 篇相关的同行评审应用研究，根据选择标准缩小到 132 篇实证研究论文进行评审。进行传统的内容分析是为了解决我们的目标，并辅以上下文中的关键字分析。结果：我们将调查结果分为以下五类技术：社交媒体（53/132，40.1％）、智能手机（37/132，28％）、传感技术（20/132，15.1％）、聊天机器人（5/ 132, 3.8%) 和其他 (17/132, 12.9%)。大多数举措都是针对检测和诊断。大多数论文讨论隐私，主要是在尊重研究参与者的隐私方面。在这种情况下，关于隐私的讨论相对较少。少数研究直接（10/132，7.6%）和间接（10/132，7.6%）讨论伦理学。尽管顺便讨论了一些法律问题（7/132，5.3%），例如用户主体的权利和隐私法的遵守情况，但任何研究都没有对法律问题进行实质性讨论。结论：在心理健康倡议中算法和数据驱动技术的实证研究中，伦理和法律问题往往没有得到明确解决。学者们可能在道德委员会或机构审查委员会阶段考虑过道德或法律问题。如果是这样，这种考虑很少出现在应用研究的已发表材料中。详细介绍该领域应用研究的同行评审论文的形式本身很可能排除对道德和法律的实质性关注。无论如何，我们发现了一些问题，包括心理健康服务用户几乎完全缺乏参与、很少考虑算法问责制，以及过度医疗化和技术解决主义的可能性。大多数论文是在计算机科学领域的试点或探索阶段发表的。因此，这些技术可以以罕见的方式应用于实践，并产生严重的法律和道德影响。