This research delves into the reduction of machine learning model bias through Ensemble Learning. Our rigorous methodology comprehensively assesses bias across various categorical variables, ultimately revealing a pronounced gender attribute bias. The empirical evidence unveils a substantial gender-based wage prediction disparity: wages predicted for males, initially at \$902.91, significantly decrease to \$774.31 when the gender attribute is alternated to females. Notably, Kullback-Leibler divergence scores point to gender bias, with values exceeding 0.13, predominantly within tree-based models. Employing Ensemble Learning elucidates the quest for fairness and transparency. Intriguingly, our findings reveal that the stacked model aligns with individual models, confirming the resilience of model bias. This study underscores ethical considerations and advocates the implementation of hybrid models for a data-driven society marked by impartiality and inclusivity.

翻译：本研究深入探讨通过集成学习减少机器学习模型偏差的方法。我们采用严谨的方法论，全面评估了各类别变量中的偏差，最终揭示了显著的性别属性偏差。实证证据表明，基于性别的工资预测存在显著差异：当性别属性为男性时，预测工资初始为902.91美元，而当性别属性切换为女性时，预测工资显著下降至774.31美元。值得注意的是，库尔贝克-莱布勒散度得分指向性别偏差，其值超过0.13，主要集中于基于树的模型中。应用集成学习阐明了追求公平与透明度的路径。有趣的是，我们的发现表明，堆叠模型与个体模型保持一致，证实了模型偏差的韧性。本研究强调伦理考量，并倡导在追求公正与包容的数据驱动社会中实施混合模型。