  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.
