MSc Thesis Proposal " Adila: Fairness-informed Collaborative Team Formation" By: Hamed Ghasr Loghmani

Abstract:
Team formation aims to automate forming teams of experts who can successfully solve difficult tasks, which have firsthand effects on creating organizational performance. While
existing neural team formation methods can efficiently analyze massive collections of experts to form effective collaborative teams, they largely ignore the fairness in recommended teams of experts. Fairness breeds innovation and increases teams’ success by enabling a stronger sense of community, reducing conflict, and stimulating more creative thinking. We study the application of state-of-the-art re-ranking algorithms to mitigate the potential bias in the neural team formation models based on demographic parity and equality of opportunity. Our early experiments show that, first, neural team formation models are biased. Second, we are able to mitigate bias using re-ranking algorithms, with no or negligible loss in the efficacy of teams. The code to reproduce the experiments is available at https://github.com/fanilab/ Adila ( عادل ه , a feminine Arabic given name meaning just and fair.)

Thesis Committee:
Internal Reader: Dr. Boubakeur Boufama
External Reader: Dr. Natalie Delia, Interdisciplinary and Critical Studies, Faculty
of Arts, Humanities & Social Sciences
Advisor: Dr. Hossein Fani