Doctoral Dissertation Research: An Ethnographic Study of the Making of Mental Health Care Artificial Intelligence

这项多地点人种学调查将研究科技公司如何设计人工智能（AI）来提供心理健康诊断和治疗护理。是什么因素引导了将精神卫生保健转化为算法的许多决策，即由人工智能执行的一套指令？这些公司如何通过算法将心理健康变成可测量和/或可治疗的对象，存在巨大差异。面部表情、语调、用词或智能手机使用模式成为传达心理健康状态的潜在数据来源。换句话说，公司不只是收集数据；他们确定什么是数据来源，并建立这些数据与心理健康状态的相关性，他们以非常不同的方式这样做。除了提供资金培训一名人类学研究生掌握严格数据收集和分析的科学方法外，该项目还将向投资于发现改善精神健康诊断和治疗的更有效手段的组织广泛传播其研究结果。瓦莱丽·布莱克在加州大学伯克利分校凯伦·中村博士的指导下，将探索科技公司如何设计用于心理健康诊断和治疗的人工智能。由收集和分析心理健康数据的公司开发的算法通常是专有的。由于这些算法是在人工智能可以决定从银行贷款资格到刑事判决等一系列问题的时代开发的，因此越来越多的人呼吁人工智能决策透明。该项目询问美国和日本（两个相互关联的人工智能开发中心）的心理健康技术公司如何解决这一问题，以及这将如何影响他们使用人工智能提供护理的方式。研究将在三家人工智能公司（两家在美国，一家在日本）进行，这些公司都属于尚未得到充分研究的初创技术领域。数据收集包括对创新者和投资者、心理服务主管、工程师和开发人员的访谈和参与观察。将对数据进行分析，以确定精神卫生保健算法的目标、伦理和实际考虑以及关系。这项人种学调查旨在进一步科学地理解人工智能的不同配置和方法如何使一系列不同（有时是冲突的）目标、策略和核心信念成为可能，这些目标、策略和核心信念构成了照顾心理健康的最佳方式。该项目将在人类学、心理学和STS的交叉点上推进关于医疗保健数据化的理论理解，以及如何将不同文化地域的精神保健经验转化为算法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This multi-sited ethnographic investigation will examine how technology companies design artificial intelligence (AI) to provide mental health diagnostic and therapeutic care. What factors guide the many decisions that translate mental health care into algorithms, a set of instructions carried out by AI? There is tremendous variation in how these companies make mental health into an object that is measurable and/or treatable via algorithm. Facial expressions, vocal tone, word choice, or smartphone usage patterns become potential sources of data that communicate mental health states. In other words, companies do not simply collect data; they determine what counts as data sources and establish the correlation of this data to mental health states, and they are doing so in very different ways. In addition to providing funding for the training of a graduate student in anthropology in scientific methods of rigorous data collection and analysis, the project would broadly disseminate its findings to organizations invested in discovering more effective means of improving mental health diagnosis and treatment.Valerie Black, under the supervision of Dr. Karen Nakamura at the University of California at Berkeley, will explore how technology companies design AI for mental health diagnosis and treatment. The algorithms developed by companies that gather and analyze mental health data are typically proprietary. Since these algorithms are being developed at a time when AI can determine matters ranging from bank loan eligibility to criminal sentencing, there is a growing call for AI decision-making transparency. This project asks how mental health technology companies in the US and Japan (two major, interconnected centers of AI development) are addressing this concern, and how that impacts the ways in which they use AI to provide care. Research will take place at three AI companies (two in the U.S. and one in Japan) in the understudied startup technology sector. Data collection includes interviews and participant observation with innovators and investors, psychological service leads, and engineers and developers. Data will be analyzed to ascertain what objectives, ethical and practical considerations, and relationships inform the mental health care algorithms. This ethnographic investigation aims to further scientific understanding of how different configurations of and approaches to AI make possible an array of different (and sometimes clashing) objectives, strategies, and core beliefs about what constitutes optimal ways of caring for mental health. The project will advance theoretical understanding at the intersections of anthropology, psychology, and STS around datafication in health care and how mental care, as experienced across different cultural terrains, is translated into algorithms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.