{"id":1476,"date":"2022-09-29T17:07:30","date_gmt":"2022-09-29T21:07:30","guid":{"rendered":"https:\/\/www.chiwork.org\/?p=1476"},"modified":"2022-10-06T10:25:39","modified_gmt":"2022-10-06T14:25:39","slug":"assembling-teams-using-recommender-systems-the-effects-of-algorithms-and-agency-on-team-formation","status":"publish","type":"post","link":"https:\/\/chiwork.org\/23\/online-conversations\/assembling-teams-using-recommender-systems-the-effects-of-algorithms-and-agency-on-team-formation\/","title":{"rendered":"Assembling Teams Using Recommender Systems: The Effects of Algorithms and Agency on Team Formation"},"content":{"rendered":"\n

Join us for a CHIWORK conversation on October 6, 2022 at 11am EST titled “Assembling Teams Using Recommender Systems: The Effects of Algorithms and Agency on Team Formation”<\/strong>.<\/p>\n\n\n\n

Can technologies help organizations assemble more effective and diverse teams? This study seeks to understand how recommender systems can help individuals assemble functionally diverse teams. We run a 2×2 between-subject laboratory experiment with 384 participants to test the effect of a recommender system\u2019s algorithms on individuals\u2019 teaming experience, team processes, and team performance. This experiment controls two main factors: personal agency (i.e., when users can choose their teammates), and algorithmic diversity (i.e., an algorithm that maximizes diversity in teammate recommendations). Preliminary results show that personal agency alone negatively affects teams\u2019 performance and functional diversity. In contrast, combining personal agency with algorithmic diversity helps participants assemble teams with high functional diversity levels and achieve better task performance. This study provides an example of how shepherding users by combining their agency with diversity criteria helps them assemble more effective teams. <\/p>\n\n\n\n

Speaker: Diego G\u00f3mez-Zar\u00e1 <\/h3>\n\n\n\n
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Assistant Professor in Computer Science and Engineering, University of Notre Dame<\/h4>\n\n\n\n

Professor G\u00f3mez-Zar\u00e1’s research focuses on how social computational systems help people organize and collaborate. His work has been at the forefront of computational social science, human-computer interaction, and network science. His recent publications include work in recommender systems, team formation, team formation, diversity, and virtual reality. This research has won best paper awards at top conferences in human-computer interaction, including CHI, CSCW, and IUI. His dissertation research was awarded the 2020 Microsoft Research Dissertation Grant, the NSF\u2019s SES Dissertation Improvement, and the INGRoup J. Richard. Hackman Award. He served as a graduate student representative on the ACM CSCW Steering committee. Learn more about Diego G\u00f3mez-Zar\u00e1 at: https:\/\/www.dgomezara.cl<\/a><\/p>\n<\/div>\n\n\n\n

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Conversation Details:<\/h3>\n\n\n\n