Collaborative Research: FW-HTF-RL: Understanding the Ethics, Development, Design, and Integration of Interactive Artificial Intelligence Teammates in Future Mental Health Work

该研究项目是对全国缺乏熟练掌握研究支持的治疗方案的精神卫生工作者的回应。研究人员试图了解人工智能（AI）的最新创新如何有效地和道德地解决和缓解对心理健康治疗的未满足需求。心理健康工作者包括几个相关的职业，包括临床心理学家，社会工作者和咨询师。这种规模不足的劳动力迫切需要可扩展和有效的技能提升，以促进研究支持的治疗方案的广泛和常规实施。提高劳动力的技能受到限制，因为没有足够的专家培训人员来保持心理健康工作者精通现有的最佳做法。这些劳动力主要依赖于最初的人与人之间的培训（例如，研究生院），然后在整个职业生涯中进行相对最少的后续观察和反馈。因此，数百万有精神健康问题的美国人无法获得有效的、研究支持的护理。心理健康工作人员将受益于帮助临床医生学习和持续使用研究支持的治疗方案的技术。对于这一需求来说，现代人工智能系统已经发展到这样一个地步，即该技术可以被视为高技能工作环境中的队友，而不仅仅是一种数据处理工具。整合人工智能的最新进展，跨学科的研究人员团队将开发一个交互式人工智能系统，可以快速评估心理健康工作者与患者的表现，为工作者提供可操作的反馈，并接收来自工作者的输入，以便反馈基于个人工作者需要学习的内容。这个计算系统被称为可信赖、可解释和自适应的人工智能团队监控机（TEAMMAIT），它将作为一个客观、非判断性和保密的同事，可以在一段时间内提供个性化的反馈。这种类型的Worker-AI团队有可能通过减少对成本高昂且几乎不可用的人对人培训的依赖来改变技能提升过程。虽然这个项目的重点是心理健康工作，由于关键的未满足的需求，从这个项目的见解可以推广到其他医疗保健和教育背景。这个项目汇集了几个学科，包括临床心理学，工业组织心理学，人机交互，和信息科学。该团队的结构是为了实现多个趋同目标。首先，研究人员的目标是更好地了解引入Worker-AI团队将如何影响心理健康工作者的预期能力，包括如何与AI合作并应对风险。其次，研究人员的目标是获得有关如何在心理健康工作中设计人工智能团队成员的见解，以促进道德和有效的人工智能团队合作。第三，研究人员的目标是学习如何开发和部署AI队友，以提高心理健康工作人员的技能。TEAMMAIT的原型将在不同的环境中进行评估，并与不同的工作人员和不同的患者人群。从原型用户收集的数据将为心理健康工作中的Worker-AI Teaming提供一套开发指南，以及开发和使用这些系统的一套可推广的道德准则。与用户的访谈将深入了解心理健康工作场所如何为Worker-AI Teaming做好最好的准备，并优化其使用，同时保持工人的福祉和高质量的临床护理。该研究计划将提供见解，这将有助于使精神卫生工作者在现实世界的诊所中提高技能更具可扩展性和有效性，改善美国各地不同患者群体获得最佳实践的机会。该项目由人类技术前沿跨部门计划的未来工作资助，以促进对相互依赖的人类的更深入的基本理解。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的评估，被认为值得支持。影响审查标准。

This research project is a response to the national shortage of mental health workers who are skilled in research-supported treatment protocols. The investigators seek to understand how recent innovations in artificial intelligence (AI) can effectively and ethically address and mitigate unmet demands for mental health treatment. Mental health workers include several related professions including clinical psychologists, social workers, and counselors. This undersized workforce is in dire need for scalable and effective upskilling in order to facilitate widespread and routine implementation of research-supported treatment protocols. Upskilling the workforce has been constrained because there are insufficient numbers of expert trainers to keep mental health workers proficient in the best available practices. This workforce has primarily relied on initial human-to-human training (e.g., graduate school) followed by relatively minimal follow-up observation and feedback throughout one’s career. As a result, millions of Americans with mental health conditions have restricted access to effective, research-supported care. The mental health workforce will benefit from technology that helps clinicians learn and sustain their use of research-supported treatment protocols. Important to this need, modern AI systems have developed to such a point where the technology can be considered a teammate in highly skilled work contexts, not simply a data processing tool. Integrating recent advancements in AI, the interdisciplinary team of investigators will develop an interactive AI system that can quickly evaluate a mental health worker’s performance with a patient, provide actionable feedback to the worker, and receive input from the worker so that feedback is based on what that individual worker needs to learn. This computational system, called the Trustworthy, Explainable, and Adaptive Monitoring Machine for AI Teams (TEAMMAIT), will function as an objective, nonjudgmental, and confidential colleague who can provide individualized feedback over a period of time. This type of Worker-AI Teaming has potential to transform the upskilling process by reducing the reliance on cost-prohibitive and scarcely available human-to-human training. While this project focuses on mental health work due to critical unmet demands, insights from this project can generalize to other healthcare and educational contexts.This project brings together several disciplines including clinical psychology, industrial-organizational psychology, human-computer interaction, and information science. The team is structured to achieve multiple convergent goals. First, the investigators aim to better understand how introducing Worker-AI Teams will impact the expected competencies of mental health workers including how to collaborate with AI and respond to risks. Second, the investigators aim to gain insights regarding how to design AI Teammates in mental health work that facilitate ethical and effective Worker-AI Teaming. And third, the investigators aim to learn how to develop and deploy AI Teammates that can upskill the mental health workforce. A prototype of TEAMMAIT will be evaluated in diverse settings and with diverse workers and diverse patient populations. Data collected from prototype users will result in a set of development guidelines for Worker-AI Teaming in mental health work, as well as a set of generalizable ethical guidelines for developing and using these systems. Interviews with users will provide insights into how mental health workplaces can best prepare for Worker-AI Teaming and optimize its use while maintaining worker well-being and high-quality clinical care. The research plan will provide insights that will help make mental health worker upskilling more scalable and effective in real-world clinics, improving access to best practices for diverse patient populations across the United States. This project has been funded by the Future of Work at the Human-Technology Frontier cross-directorate program to promote deeper basic understanding of the interdependent human-technology partnership in work contexts by advancing the design of intelligent work technologies that operate in harmony with human workers.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.