  This paper presents a Fuzzy Cognitive Map model to quantify implicit bias in
structured datasets where features can be numeric or discrete. In our proposal,
problem features are mapped to neural concepts that are initially activated by
experts when running what-if simulations, whereas weights connecting the neural
concepts represent absolute correlation/association patterns between features.
In addition, we introduce a new reasoning mechanism equipped with a
normalization-like transfer function that prevents neurons from saturating.
Another advantage of this new reasoning mechanism is that it can easily be
controlled by regulating nonlinearity when updating neurons' activation values
in each iteration. Finally, we study the convergence of our model and derive
analytical conditions concerning the existence and unicity of fixed-point
attractors.
