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

合作研究：FW-HTF-RL：了解未来心理健康工作中交互式人工智能队友的伦理、开发、设计和整合

• 批准号: 2326144
• 负责人: Andrew Sherrill
• 金额: $ 54.83万
• 依托单位: Emory University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326144&HistoricalAwards=false
• 关键词: Collaborative; Research; FW; HTF; RL

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.

这项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)最近的创新如何有效和合乎伦理地解决和缓解心理健康治疗方面未得到满足的需求。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。这一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。劳动力技能的提升一直受到限制，因为没有足够数量的专家培训师来保持精神卫生工作者熟练掌握可用的最佳做法。这支队伍主要依靠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较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限制了获得有效的、由研究支持的护理的机会。精神卫生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。对这一需求很重要的是，现代人工智能系统已经发展到这样一种程度，在高技能的工作环境中，这项技术可以被视为团队合作伙伴，而不仅仅是一种数据处理工具。结合人工智能领域的最新进展，跨学科的调查团队将开发一种交互式人工智能系统，该系统可以快速评估精神卫生工作者与患者的表现，向工作者提供可操作的反馈，并从工作者那里接收输入，以便反馈基于个体工作者需要学习的内容。这个计算系统被称为AI团队的值得信赖、可解释和自适应的监控机器(TEAMMAIT)，它将作为一个客观、非评判和保密的同事发挥作用，可以在一段时间内提供个性化的反馈。这种类型的工人-人工智能团队有可能通过减少对成本高昂且几乎无法获得的人与人之间的培训的依赖，来改变提高技能的过程。虽然由于关键的未得到满足的需求，该项目专注于心理健康工作，但该项目的见解可以推广到其他医疗保健和教育背景。该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。团队的结构是为了实现多个汇聚目标。首先，调查人员的目标是更好地了解引入工人-人工智能团队将如何影响心理健康工作者的预期能力，包括如何与人工智能合作和应对风险。其次，调查人员的目标是获得关于如何在心理健康工作中设计人工智能队友的见解，以促进道德和有效的工人-人工智能团队。第三，调查人员的目标是学习如何发展和部署能够提高精神卫生工作人员技能的人工智能队友。TEAMMAIT的原型将在不同的环境中进行评估，并与不同的工人和不同的患者群体一起进行评估。从原型用户收集的数据将产生一套工人-人工智能团队在精神健康工作中的发展指南，以及一套开发和使用这些系统的可概括的伦理指南。对用户的采访将为心理健康工作场所如何最好地为工人-AI合作做好准备并优化其使用，同时保持工人的福祉和高质量的临床护理提供洞察。该研究计划将提供见解，帮助精神卫生工作者在现实世界的诊所提高技能，提高可伸缩性和有效性，改善全美不同患者群体获得最佳实践的机会。该项目由人类-技术前沿交叉部门工作未来计划资助，旨在通过推进与人类工人和谐运作的智能工作技术的设计，促进对工作环境中相互依赖的人类-技术伙伴关系的更深层次的基本了解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。