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

Measuring Implicit Bias Using SHAP Feature Importance and Fuzzy Cognitive Maps