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，将特征重要性概念与隐式偏见相结合。通过包含以下三个步骤的方法论实现：（i）构建分类器并优化其超参数，（ii）构建能够量化隐式偏见的模糊认知图模型，（iii）利用SHAP特征重要性在仿真过程中激活神经概念。基于公平性研究的真实案例结果支持我们的双重假设：一方面，揭示了将特征重要性方法作为衡量隐式偏见绝对工具的风险；另一方面，得出结论：对受保护特征的偏见程度可能因数值编码或类别编码方式的不同而存在差异。