Understanding and Addressing Gender Bias in Expert Finding Task

TL;DR

This paper investigates gender bias in expert finding models on StackOverflow, revealing biases favoring male users and proposing methods to improve gender fairness without losing accuracy.

Contribution

It is the first study to detect and mitigate gender bias in expert finding models, introducing balanced preprocessing and content/social network integration.

Findings

Models favor male users due to activity and reputation biases.

Proposed adjustments improve gender balance significantly.

Fairer models maintain comparable accuracy.

Abstract

The Expert Finding (EF) task is critical in community Question&Answer (CQ&A) platforms, significantly enhancing user engagement by improving answer quality and reducing response times. However, biases, especially gender biases, have been identified in these platforms. This study investigates gender bias in state-of-the-art EF models and explores methods to mitigate it. Utilizing a comprehensive dataset from StackOverflow, the largest community in the StackExchange network, we conduct extensive experiments to analyze how EF models' candidate identification processes influence gender representation. Our findings reveal that models relying on reputation metrics and activity levels disproportionately favor male users, who are more active on the platform. This bias results in the underrepresentation of female experts in the ranking process. We propose adjustments to EF models that incorporate a more balanced preprocessing strategy and leverage content-based and social network-based information, with the aim to provide a fairer representation of genders among identified experts. Our analysis shows that integrating these methods can significantly enhance gender balance without compromising model accuracy. To the best of our knowledge, this study is the first to focus on detecting and mitigating gender bias in EF methods.