Date of Graduation

12-2021

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Computer Science (PhD)

Degree Level

Graduate

Department

Computer Science & Computer Engineering

Advisor/Mentor

Gauch, Susan E.

Committee Member

Panda, Brajendra N.

Second Committee Member

Andrews, David L.

Third Committee Member

Cronan, Timothy P.

Keywords

demographics; diversity ranking; algorithms; bias; academia

Abstract

The goal of group formation is to build a team to accomplish a specific task. Algorithms are being developed to improve the team's effectiveness so formed and the efficiency of the group selection process. However, there is concern that team formation algorithms could be biased against minorities due to the algorithms themselves or the data on which they are trained. Hence, it is essential to build fair team formation systems that incorporate demographic information into the process of building the group. Although there has been extensive work on modeling individuals’ expertise for expert recommendation and/or team formation, there has been relatively little prior work on modeling demographics and incorporating demographics into the group formation process.

We propose a novel method to represent experts’ demographic profiles based on multidimensional demographic features. Moreover, we introduce three diversity ranking algorithms that form a group by considering demographic features along with the minimum required skills. Unlike many ranking algorithms that consider one Boolean demographic feature (e.g., gender or race), our diversity ranking algorithms consider multiple demographic features simultaneously. Finally, we introduce a fair team formation algorithm that balances each candidate's demographic information and expertise. We evaluate our proposed algorithms using real datasets based on members of a computer science program committee. The result shows that our algorithms form a program committee that is more diverse with an acceptable loss in utility.

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