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

Syracuse University, Syracuse NY

Investigators

Abstract

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.