Machine learning models are increasingly used in critical decision-making applications. However, these models are susceptible to replicating or even amplifying bias present in real-world data. While there are various bias mitigation methods and base estimators in the literature, selecting the optimal model for a specific application remains challenging. This paper focuses on binary classification and proposes FairGridSearch, a novel framework for comparing fairness-enhancing models. FairGridSearch enables experimentation with different model parameter combinations and recommends the best one. The study applies FairGridSearch to three popular datasets (Adult, COMPAS, and German Credit) and analyzes the impacts of metric selection, base estimator choice, and classification threshold on model fairness. The results highlight the significance of selecting appropriate accuracy and fairness metrics for model evaluation. Additionally, different base estimators and classification threshold values affect the effectiveness of bias mitigation methods and fairness stability respectively, but the effects are not consistent across all datasets. Based on these findings, future research on fairness in machine learning should consider a broader range of factors when building fair models, going beyond bias mitigation methods alone.

本研究专注于二元分类，提出了一种名为FairGridSearch的新型框架，用于比较提升公平性的模型。研究应用FairGridSearch于三个常见数据集，分析了度量选择、基础估计器选择以及分类阈值对模型公平性的影响。研究结果强调选择适当的准确性和公平性度量对模型评估的重要性，同时不同的基础估计器和分类阈值值会影响偏差缓解方法的有效性和公平性稳定性，但这些影响在不同数据集上并不一致。基于这些发现，未来研究在构建公平模型时应考虑更广泛的因素，超越单纯的偏差缓解方法。

公平网格搜索: 评估增强公平性模型的框架