In this paper, we integrate the concepts of feature importance with implicit bias in the context of pattern classification. This is done by means of a three-step methodology that involves (i) building a classifier and tuning its hyperparameters, (ii) building a Fuzzy Cognitive Map model able to quantify implicit bias, and (iii) using the SHAP feature importance to active the neural concepts when performing simulations. The results using a real case study concerning fairness research support our two-fold hypothesis. On the one hand, it is illustrated the risks of using a feature importance method as an absolute tool to measure implicit bias. On the other hand, it is concluded that the amount of bias towards protected features might differ depending on whether the features are numerically or categorically encoded.

翻译：本文在模式分类背景下，将特征重要性的概念与隐性偏见相融合。研究通过三阶段方法论实现：（i）构建分类器并调优其超参数，（ii）构建能量化隐性偏见的模糊认知图模型，（iii）在仿真过程中利用SHAP特征重要性激活神经概念。基于公平性研究真实案例的实证结果支持了我们的双重假设：一方面，揭示了将特征重要性方法作为衡量隐性偏见的绝对工具所存在的风险；另一方面，得出结论认为受保护特征产生的偏见程度可能因数值编码与类别编码方式的不同而存在差异。