About me

Kevin Crowston

Distinguished Professor of Information Science,
Syracuse University School of Information Studies

 Picture of Kevin
Kevin Crowston is a Distinguished Professor of Information Science at the Syracuse University School of Information Studies (aka the iSchool). He received his A.B. (1984) in Applied Mathematics (Computer Science) from Harvard University and a Ph.D. (1991) in Information Technologies from the Sloan School of Management, Massachusetts Institute of Technology.

His research examines new ways of organizing made possible by the use of information technology. He approaches this issue in several ways: empirical studies of coordination-intensive processes in human organizations (especially virtual organization); theoretical characterizations of coordination problems and alternative methods for managing them; and design and empirical evaluation of systems to support people working together. Specific domains of interest include citizen science projects, data science teamwork and the future of journalism.

He is most recently a PI on an NSF HCC project: "Intelligent support for non-experts to navigate large information spaces" (21-06865) and PI on an NSF FW-HTF grant, "The Future of News Work: Human-Technology Collaboration of Journalistic Research and Narrative Discovery" (21-29047). With colleagues, he headed a Research Coordination Network to develop a socio-technical perspective on work in the age of intelligent machines.

He is co-editor-in-chief of the journal Information, Technology and People and was editor-in-chief of ACM Transactions on Social Computing.

SCImago Journal & Country Rank

a word cloud of research: information, science, management

Taming artificial intelligence: A theory of control–accountability alignment among AI developers and users

ReelFramer: Human-AI Co-Creation for News-to-Video Translation

Wang, S., Menon, S., Long, T., Henderson, K., Li, D., Crowston, K., et al.. (2024). ReelFramer: Human-AI Co-Creation for News-to-Video Translation. In CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3613904.3642868

Folksonomies in crowdsourcing platforms: Three tensions associated with the development of shared language in distributed groups

Bullard, J., Crowston, K., Jackson, C. B., Smith, A. O., & Østerlund, C.. (2024). Folksonomies in crowdsourcing platforms: Three tensions associated with the development of shared language in distributed groups. In The European Conference on Computer-Supported Cooperative Work (ECSCW). https://doi.org/10.48340/ecscw2024_n06

Mutual learning in human-AI interaction

Østerlund, C., Crowston, K., Jackson, C. B., Takou-Ayaoh, M., Wu, Y., & Katsaggelos, A. K.. (2024). Mutual learning in human-AI interaction. In Trust and Reliance in Evolving Human-AI Workflows (TREW) Workshop, ACM CHI Conference. Presented at the Trust and Reliance in Evolving Human-AI Workflows (TREW) Workshop, ACM CHI Conference, Honolulu, HI.

Framing and feelings on social media: The futures of work and intelligent machines

Öcal, A., & Crowston, K.. (2024). Framing and feelings on social media: The futures of work and intelligent machines. Information, Technology & People. https://doi.org/10.1108/ITP-01-2023-0049

Implications for hybrid newswork from the activities of local US television journalists during COVID

Gravity Spy: Lessons Learned and a Path Forward

Zevin, M., Jackson, C. B., Doctor, Z., Wu, Y., Østerlund, C., L. Johnson, C., et al.. (2024). Gravity Spy: Lessons Learned and a Path Forward. European Physical Journal Plus, 139, Article 100. https://doi.org/10.1140/epjp/s13360-023-04795-4

Making sense of AI systems development

Dolata, M., & Crowston, K.. (2024). Making sense of AI systems development. Ieee Transactions On Software Engineering, 50(1), 123–140. https://doi.org/10.1109/TSE.2023.3338857

Surveys Provide Insight Into Three Factors That Encourage Open Data and Science

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