FW-HTF-RL: Collaborative Research: Future expert work in the age of "black box", data-intensive, and algorithmically augmented healthcare

专家工作的性质正在发生变化。人工智能和数据科学等技术进步越来越多地使新的计算机化工具和产品能够作出以前由人类专家作出的预测和建议。然而，这些新工具中的许多都是“黑匣子”，它们的内部工作原理往往不被用户理解，提出了造成认知负荷的需求，并淡化了抽象问题的解决。随着这些技术的部署，人们对它们如何影响专家的工作实践、对工作价值的看法以及专家与客户的关系知之甚少。需要进行基础研究，以便在数据密集型增强认知的时代理解和改进工作，特别是在医疗保健领域，因为这种新技术正在迅速改变专家的工作。该项目预计将通过技术、工作流程和交互的组合重新设计来改变专家工作的未来。它将导致：更健康、更知情的人群；在重组的医疗保健职业中有效地部署人的能力；医疗保健提供者减少花在重复性任务上的时间比例，同时增加用于增值、有意义活动的时间；关于设计和提供认知增强专家建议的指导方针；以及精通工作场所认知增强技术的跨学科研究的学生。该项目的目标是：i)以多学科的方式研究专家、患者和技术之间的关系；ii)开发这些技术服务于专家和客户的新方法；三)使专家工作更具响应性、附加值和意义。该项目包括两股。在“理解”一节中，我们考察了专家、客户和认知增强技术之间的互动。在“形状”方面，该项目为技术和组织干预奠定了基础，这些干预将使专家、客户和技术之间的互动更加有效和授权。该项目的多学科团队包括计算机科学、人机交互、动力系统和组织的研究人员以及医学临床医生，该项目将有助于：i)量化认知增强交互的好处和缺点，以及测量专家、客户和认知增强技术之间的关系中的信息流的可扩展方法；ii)洞察何时、为什么以及如何将认知增强技术体验为专业知识增强而不是退化；iii)数据驱动的方法，以预测技术和组织干预对专家工作和专家与患者互动的影响；Iv)让专家和客户与黑盒认知互动的新工具和工作流程--增强技术；v)建立问题表征如何嵌入专家的模型；vi)系统地探索解释和对话干预如何影响专家的工作和专家与客户的关系。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。

The nature of expert work is changing. Technological advances such as artificial intelligence and data science increasingly enable new computerized tools and products that make predictions and recommendations which were previously made by human experts. However, many of these new tools are "black boxes" whose inner workings are often not understood by their users, place demands that create cognitive load, and de-emphasize abstract problem solving. As these technologies are being deployed, there is little understanding of how they affect experts' work practices, perceptions of the value of work, and the expert-client relationship. Foundational research is needed in order to understand and improve work in an age of data-intensive enhanced cognition, especially in healthcare where such new technologies are rapidly changing expert work. This project is expected to transform the future of expert work through a combined redesign of technology, workflow, and interactions. It will lead to: a healthier and better-informed population; efficient deployment of human capabilities in restructured healthcare occupations; healthcare providers reducing the proportion of time spent on repetitive tasks while increasing time devoted to value-adding, meaningful activities; guidelines on design and delivery of cognition-augmenting expert advice; and students who are well versed in cross-disciplinary research on cognition-augmenting technologies in the workplace.The project's goals are: i) to study the relationships between experts, patients, and technologies in a multidisciplinary way; ii) to develop new ways for these technologies to serve experts and clients; and iii) to make expert work more responsive, value-adding, and meaningful. The project includes two strands. In the "Understand" strand, the interactions between experts, clients and cognition-augmenting technologies are examined. In the "shape" strand, the project lays the foundations for technological and organizational interventions that will make the interactions between experts, clients, and technology more effective and empowering. With a multidisciplinary team including researchers in computer science, human-computer interaction, dynamical systems, and organization alongside with medical clinicians, the project will contribute: i) scalable approaches toward quantifying the benefits and drawbacks of cognition-augmented interactions, as well as measuring information flow in relationships between experts, clients, and cognition augmenting technologies; ii) insights into when, why, and how cognition-augmenting technologies are experienced as expertise enhancing, rather than degrading; iii) data-driven methodologies to predict the effects of technical and organizational interventions on experts' work and experts' interaction with patients; iv) novel tools and workflows for experts and clients to interact with black-box cognition-augmenting technologies; v) modeling how representation of problems can be embedded in expert; and vi) systematic exploration of explanation and dialogue interventions with regard to how they affect experts' work and expert-client relationship.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.