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

FW-HTF-RL: Collaborative Research: Future expert work in the age of "black box", data-intensive, and algorithmically augmented healthcare
FW-HTF-RL:协作研究:“黑匣子”、数据密集型和算法增强医疗保健时代的未来专家工作
批准号:
1928586
负责人:
Mark Riedl
金额:
$49.87万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-09-01 至 2024-08-31

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中文摘要
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英文摘要
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.
期刊论文(10)
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会议论文
DOI: 10.1145/3411763.3441342
发表时间: 2021-05
期刊: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems
影响因子: --
作者: [Upol Ehsan;Philipp Wintersberger;Q. Liao;Martina Mara;M. Streit;Sandra Wachter;A. Riener;Mark O. Riedl]
通讯作者: Upol Ehsan;Philipp Wintersberger;Q. Liao;Martina Mara;M. Streit;Sandra Wachter;A. Riener;Mark O. Riedl
Social Construction of XAI: Do We Need One Definition to Rule Them All?
XAI 的社会构建:我们需要一个定义来统治它们吗?
DOI: --
发表时间: 2022
期刊: NeurIPS workshop on Human-centered AI
影响因子: --
作者: [Ehsan, Upol, Riedl, Mark O.]
通讯作者: Riedl, Mark O.
DOI: 10.1007/978-3-030-60117-1_33
发表时间: 2020-02
期刊: ArXiv
影响因子: --
作者: [Upol Ehsan;Mark O. Riedl]
通讯作者: Upol Ehsan;Mark O. Riedl
Seamful XAI: Operationalizing Seamful Design in Explainable AI
Seamful XAI:在可解释的 AI 中实施 Seamful 设计
DOI: --
发表时间: 2024
期刊: Proceedings of the ACM Conference on Computer Supported and Collaborative Work
影响因子: --
作者: [Upol Ehsan, Qingzi Vera Liao, Samir Passi, Mark O. Riedl, Hal Daume]
通讯作者: Hal Daume
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