TY - JOUR
T1 - Measuring Implicit Bias Using SHAP Feature Importance and Fuzzy Cognitive Maps
AU - Grau, Isel
AU - Nápoles, Gonzalo
AU - Hoitsma, Fabian
AU - Koutsoviti Koumeri, Lisa
AU - Vanhoof, Koen
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
U2 - 10.48550/arXiv.2305.09399
DO - 10.48550/arXiv.2305.09399
M3 - Article
SN - 2331-8422
VL - 2023
SP - 1
EP - 20
JO - arXiv
JF - arXiv
M1 - 2305.09399v2
ER -