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.

翻译：专家发现（EF）任务在社区问答（CQ&A）平台中至关重要，它通过提高答案质量和缩短响应时间显著增强了用户参与度。然而，这些平台中已被识别出存在偏见，尤其是性别偏见。本研究调查了最先进的EF模型中的性别偏见，并探索了缓解该偏见的方法。利用来自StackExchange网络中最大的社区StackOverflow的综合数据集，我们进行了广泛的实验，以分析EF模型的候选人识别过程如何影响性别代表性。我们的研究结果表明，依赖声誉指标和活跃度水平的模型不成比例地偏向于在平台上更活跃的男性用户。这种偏见导致女性专家在排名过程中代表性不足。我们提出了对EF模型的调整方案，这些方案结合了更平衡的预处理策略，并利用了基于内容和基于社交网络的信息，旨在为识别出的专家提供更公平的性别代表性。我们的分析表明，整合这些方法可以在不损害模型准确性的情况下显著改善性别平衡。据我们所知，本研究是首个专注于检测和缓解EF方法中性别偏见的研究。