Diverse Group Formation Based on Multiple Demographic Features
Mohammed Alqahtani, Susan Gauch, Omar Salman, Mohammed Ibrahim, Reem, Al-Saffar

TL;DR
This paper introduces a novel approach for forming diverse groups by considering multiple demographic features simultaneously, aiming to enhance fairness and diversity in team formation algorithms.
Contribution
It proposes a new method to model demographic profiles and two diversity ranking algorithms that incorporate multiple demographic attributes into team formation.
Findings
Algorithms produce more diverse groups with acceptable utility loss.
Multidimensional demographic features improve diversity in team formation.
Evaluation on real dataset demonstrates effectiveness of the proposed methods.
Abstract
The goal of group formation is to build a team to accomplish a specific task. Algorithms are employed to improve the effectiveness of the team 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…
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Expert finding and Q&A systems · Privacy-Preserving Technologies in Data
