Modeling implicit bias with fuzzy cognitive maps

Gonzalo Nápoles, Isel Grau, Leonardo Concepción, Lisa Koutsoviti Koumeri, João Paulo Papa

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Pages (from-to)33-45
Number of pages13
Publication statusPublished - 7 Apr 2022


  • Fairness
  • Implicit bias
  • Fuzzy cognitive maps
  • Convergence analysis


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