Computer-Supported Collaboration

Dr. Kane's work in the area of computer-supported collaboration takes a human-centered approach. A dominant perspective is that teams tackling complex problem benefit from the using computer-mediated technologies to share information among analysts separated by distance and time (Bruns, 2012). However, National Science Foundation supported research conducted by Dr. Kane in collaboration with Carnegie Mellon University Professor Sara Kiesler and then-graduate student Rougu Kang suggests that such optimism be tempered (Kane, Kielser, & Kang, 2018; Kang, Kane, & Kiesler, 2014). Experiments reveal that analysts often experience “teammate inaccuracy blindness,” mistaking inaccurate information as helpful and performing worse than counterparts who receive only raw data from remote collaborators (Kang, Kane, & Kiesler, 2014). Although adding accurate information from an additional collaborator can help overcome the tendency to rely on misinformation (Kang, Kane, & Kiesler, 2014), for the controversy or inconsistency to be useful it would need to be noticed. A series of experiments that employ an evaluation prompt designed to increase attention to collaborator’s information show that inaccuracy blindness is quite challenging to counteract and poses a timely challenge for the researchers and practitioners alike (Kane, Kielser, & Kang, 2018).

Dr. Kane was a co-PI on a project examining how intelligence analysts can work together with artificial intelligence to overcome inaccuracy blindness and other collaboration issues. This project is funded by a grant from the Army Research Office to an interdiciplinary team including PI Dr. Susannah Paletz, College of Information at the University of Maryland and fellow co-PI Adam Porter in the Department of Computer Science at the University of Maryland and the Fraunhofer USA Center of Mid-Atlantic. The team interviewed intelligence analysts to generate an inductive model of sensemaking in intelligence analysis shift handovers (Kane et al., 2023), used those insights to developed a testbed, Human-Agent Teaming on Intelligence Tasks (HATIT) platform, for examining the effects of AI on shift handover (Paletz et al., 2025), conducted between-subjects experiments examining the effects of AI Summarizers on collaborative analysis processes and outcome (Kane et al., 2025), and explored how AI may support knowledge work teams (Kane et al., 2026).  

Using the HATIT platform, Kane et al. (2025) investigated how different types of AI summarizers influence virtual collaborative analysis. The study specifically compared two AI summarizers: an indicative summarizer that outlines the content (like a move trailer) and an informative AI summarizer that provides a condensed version (like a plot synopsis). Participant's baseline dispositional trust in AI influenced their initial trust, while learned trust in the AI summarizer increased over time in the informative but not the indicative AI condition. While the AI summarizer type did not influence problem-solving accuracy, it shaped how quickly and often participants switched attention to teammate provided information. Those in the informative AI summarizer condition displayed faster but less frequently attention-switching.

Drs. Kane, Paletz, Diep, and Porter have continued work in this area with additional collaborators including University of Maryland, Information School post-doctoral researcher Rohit Mallick and graduate students, Kimberly Do and Ciara Fabian, further examining collective judgment formation (Mallick et al., 2026) and sensemaking. 

References

Kane, A.A., Diep, M.  Porter, A.A., Paletz, S. B. F. (2026). Operationalizing and optimizing human-agent teaming for knowledge work. In. S.B.F Paletz and Dubrow, S. (Eds.) AI in Teams (pp. 115–131). Research on Managing Groups and Teams Series, Vol. 21, Emerald.  https://doi.org/10.1108/S1534-085620260000021018 

Kane, A. A., Kiesler, S., & Kang, R. (2018). Inaccuracy blindness in collaboration persists, even with an evaluation prompt. CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1-9. doi: 10.1145/3173574.3174068

Kane, A. A., Paletz, S. B. F., Diep, M., Hajkowski, A., Porter, A. A. (2025). Virtual collaborative analysis: Effects of two AI summarizers. Small Group Research. 56(5), 821-863. https://doi.org/10.1177/10464964251361563    

Kane, A.A., Paletz, S.B.F., Vahlkamp, S.H., Nelson, T., Porter, A., Diep, M. & Carraway, M. (2023) Intelligence analysis shift work: Sensemaking processes, tensions and takeaways. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 67(1), 1-6  https://doi.org/10.1177/21695067231192569 

Kang, R., Kane, A. A., & Kiesler, S. (2014). Teammate inaccuracy blindness: When information sharing tools hinder collaborative analysis. Proceedings of the Annual Meeting of Computer Supported Collaborative Work (CSCW ‘14). NY: ACM Press. doi: 10.1145/2531602.2531681

Mallick, R., Paletz, S. B. F., Kane, A. A., Do, K., Porter, A., Diep, M., & Fabian, C. A. (2026). Model of Collective Judgment Formation for Sequential Teams in Information-Dense Scenarios. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 10711813261475151. https://doi.org/10.1177/10711813261475151

Paletz, S. B. F., Kane, A. A., Diep, M., Nelson, T., Porter, A. A. & Vahlkamp, S., (2025). Human-Agent Teaming on Intelligence Tasks (HATIT): A Testbed for Evaluating AI in Intelligence Analysis. Proceedings of the 88th Annual Meeting of the American Society for Information, Science & Technology (ASIS&T) 62(1), 483–494. https://doi.org/10.1002/pra2.1272  

Updated August 2026