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: 2326146
• 负责人: Christopher Wiese
• 金额: $ 80.17万
• 依托单位: Georgia Tech Research Corporation
• 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=2326146&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.

该研究项目是针对全国缺乏熟练掌握研究支持的治疗方案的心理健康工作者的问题，研究人员试图了解人工智能 (AI) 的最新创新如何能够有效且合乎道德地解决和缓解未满足的心理健康需求。精神卫生工作者包括临床心理学家、社会工作者和咨询师等多个相关专业，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。由于没有足够的专家培训师来保持心理健康工作者精通现有的最佳实践，这支队伍主要依赖于最初的人与人之间的培训（例如研究生院），随后进行相对最少的后续观察。因此，数以百万计患有心理健康问题的美国人无法获得有效的、研究支持的护理，而心理健康工作者将受益于有助于促进学习和持续使用研究支持的治疗方案的技术。现代人工智能对这一需求很重要。系统已经发展到这样的程度，该技术可以被视为高技能工作环境中的队友，而不仅仅是一个数据处理工具。跨学科研究团队将整合人工智能的最新进展，开发一个可以快速评估数据的交互式人工智能系统。心理健康工作者对患者的表现，向工作者提供可操作的反馈，并接收工作者的输入，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“可信赖、可解释和自适应监测机器”。人工智能团队(TEAMMAIT)，将作为一个客观、不评判和保密的同事，可以在一段时间内提供个性化的反馈。这种类型的工人-人工智能团队有潜力通过减少对成本高昂且几乎没有的依赖来改变技能提升过程。虽然由于关键的未满足需求，该项目侧重于心理健康工作，但该项目的见解可以推广到其他医疗保健和教育环境。该项目汇集了多个学科，包括临床心理学、首先，研究人员旨在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。其次，研究人员的目标是深入了解如何在心理健康工作中设计 AI 队友，以促进有效的员工与人工智能团队合作。第三，研究人员的目标是了解如何开发和部署人工智能。可以提升技能的队友TEAMMAIT 的原型将在不同的环境、不同的工作人员和不同的患者群体中进行评估，从原型用户收集的数据将产生一套用于心理健康工作中的工作人员-人工智能团队的开发指南。一套用于开发和使用这些系统的通用道德准则将深入了解心理健康工作场所如何为工人人工智能团队做好最佳准备并优化其使用，同时保持工人的福祉和高质量的临床护理。计划将提供见解，帮助精神卫生工作者在现实世界的诊所中提高技能，提高其可扩展性和有效性，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。通过推进与人类工人和谐相处的智能工作技术的设计，促进对工作环境中相互依存的人类与技术伙伴关系的更深入的基本理解。该奖项是 NSF 的法定使命，并通过使用基金会的智力评估进行评估，被认为值得支持优点和更广泛的影响审查标准。