The WAIM network aims to promote disciplinary convergence in relation to work in the age of intelligent machines. The use of intelligent machines-digital technologies that feature data-driven forms of customization, learning, and autonomous action-is rapidly growing and has and will continue to impact most if not all industries and work domains. Convergence is defined by the National Science Foundation as "the deep integration of knowledge, techniques, and expertise from multiple fields to form new and expanded frameworks" (NSF 17-065). By embracing research concepts and approaches from other disciplines, researchers transform our emergent conceptual foundations and concomitant strategies in relation to the transformation of work via intelligent machines.
The conference will feature a set of panels bringing together leading researchers and practitioners from diverse approaches to demonstrate the value of convergent research on work and intelligent machines. It will as well include small-group discussions bringing together authors and a mentor for discussion around work-in-progress papers and grant proposals. These groups will provide both feedback for developing and refining convergent ideas, as well as a place to make sense of how to navigate the world's "new normal" as a researcher.
Attendance at the conference is free (though capacity limited). You can register to attend in person here: https://forms.gle/67iEsTry6VCoe6o78 In light of the recent spike in COVID transmission, at conferences in particular, masks will be strongly recommended indoors during the event and meals will be outdoors to the extent possible.
Remote participation will also be possible. Register to participate remotely here: https://forms.gle/ia5xiYvYUiyUc4V66
Travel funding (domestic advance purchase economy class airfare, ground transportation and hotel) will be provided for selected participants, with a focus on supporting doctoral students and earlier career researchers. Please contact me for details.
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Kevin Crowston
Associate Dean for Research, Distinguished Professor of Information Science
School of Information Studies
Syracuse University
+1 (315) 443.1676
crowston@syr.edu348 Hinds Hall, Syracuse, NY 13244
crowston.syr.edu
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