权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Convergence HTF: A Research Coordination Network to Converge Research on the Socio-Technological Landscape of Work in the Age of Increased Automation

Convergence HTF：一个研究协调网络，旨在融合自动化程度提高时代工作社会技术景观的研究

基本信息

批准号：
1745463
负责人：
Kevin Crowston
金额：
$ 49.98万
依托单位：
Syracuse University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-01-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1745463&HistoricalAwards=false
关键词：
Convergence HTF Research Coordination Network

项目摘要

The landscape of jobs and work is changing rapidly, driven by the development of new technologies. Intelligent, automated machines and services are a growing part of jobs and the workplace. New technologies are enabling new forms of learning, skills assessments, and job training. The potential benefits of these technologies include increased productivity and job satisfaction, and more job opportunities. But technology connected to work can also come with risks. This research coordination network (RCN) addresses the future of work at the human-technology frontier by focusing on the use of intelligent machines in work settings. The RCN supported by this award will promote convergence across computer science, engineering, and social and behavioral science disciplines to define and address key challenges and research imperatives in the future of work at the human-technology frontier with intelligent machines. This convergence RCN will employ deep integration of knowledge, theories, methods, and data from multiple fields to form new and expanded frameworks for addressing scientific and societal challenges and opportunities. The results will include the identification and sharing of new research directions and tools to reinforce positive outcomes and mitigate negative consequences of intelligent machines in work settings. Ultimately this has the power to strengthen the U.S. economy, and improve worker performance and job satisfaction.This RCN will focus on advancing the knowledge needed to develop actionable design principles that attend to both sides of the human-technology frontier in work settings that use intelligent machines. Such machines include not only autonomous robots and vehicles, but also algorithms and machine learning processes that support all types of autonomous behavior. At present, the technology side of this frontier is advancing more rapidly than the human side: people, organizations, legal frameworks, and social values, to name a few. What is necessary to bring these two side into alignment is a systems design approach that draws on both social and technological requirements as well as their interdependencies. This RCN aims to adopt this goal, thereby developing the knowledge needed to ensure that the benefits of intelligent machines are gained while the negative consequences reduced. This RCN will bring together investigators from many disciplines including computer science (artificial intelligence, machine learning), robotics, human computer interaction, cognitive science, economics, sociology, law, organizational science, ergonomics, industrial and organizational psychology, engineering, and information systems, to communicate, coordinate, and integrate their research and educational activities across disciplinary and organizational boundaries. Toward this goal, this award will support three primary RCN activities over its five-year term. First, the RCN will organize annual Convergence Conferences that will focus on the contribution of convergent research on topics regarding the socio-technological landscape of work in the age of increased automation. Second, it will support a series of workshops at different disciplinary conferences to expand the reach of the network and to consolidate, test, verify, and evolve research ideas as they develop. Third, the RCN will maintain a set of shared online resources to support the community and its research efforts.

在新技术发展的推动下，就业和工作的格局正在迅速变化。智能化、自动化的机器和服务正日益成为工作和工作场所的一部分。新技术正在推动新的学习、技能评估和职业培训形式。这些技术的潜在好处包括提高生产率和工作满意度，以及更多的就业机会。但与工作相关的技术也可能伴随着风险。这个研究协调网络(RCN)通过关注智能机器在工作环境中的使用，解决了人类技术前沿工作的未来。由该奖项支持的RCN将促进计算机科学、工程以及社会和行为科学学科的融合，以定义和解决未来人类技术前沿与智能机器工作的关键挑战和研究任务。这种融合的RCN将深度整合来自多个领域的知识、理论、方法和数据，以形成新的和扩展的框架，以应对科学和社会挑战和机遇。成果将包括确定和分享新的研究方向和工具，以加强积极成果和减轻工作环境中智能机器的负面影响。最终，这将有能力加强美国经济，提高工人的表现和工作满意度。这个RCN将专注于提高所需的知识，以开发可操作的设计原则，在使用智能机器的工作环境中照顾到人类技术前沿的两端。这类机器不仅包括自主机器人和车辆，还包括支持所有类型自主行为的算法和机器学习过程。目前，这一前沿的技术方面比人类方面发展得更快：仅举几例：人员、组织、法律框架和社会价值观。要使这两方面保持一致，必须采用一种系统设计方法，既要利用社会和技术需求，也要利用它们之间的相互依存关系。这个RCN旨在采用这一目标，从而发展所需的知识，以确保在减少负面后果的同时获得智能机器的好处。这个RCN将汇集来自许多学科的研究人员，包括计算机科学(人工智能、机器学习)、机器人学、人机交互、认知科学、经济学、社会学、法学、组织科学、人机工程学、工业和组织心理学、工程学和信息系统，以跨学科和组织边界交流、协调和整合他们的研究和教育活动。为了实现这一目标，该奖项将在其五年任期内支持RCN的三项主要活动。首先，RCN将组织一年一度的融合会议，重点是在自动化增加的时代，关于工作的社会技术前景的主题的融合研究的贡献。其次，它将在不同的学科会议上支持一系列研讨会，以扩大网络的覆盖范围，并随着研究想法的发展巩固、测试、验证和发展研究想法。第三，RCN将维护一套共享的在线资源，以支持社区及其研究努力。