Modeling implicit bias with fuzzy cognitive maps

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 connec...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2022-04, Vol.481, p.33-45
Main Authors: Nápoles, Gonzalo, Grau, Isel, Concepción, Leonardo, Koutsoviti Koumeri, Lisa, Papa, João Paulo
Format: Article
Language:English
Subjects:
Convergence analysis
Fairness
Fuzzy cognitive maps
Implicit bias
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Summary: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2022.01.070