Modeling Implicit Bias with Fuzzy Cognitive Maps
This work addresses implicit bias quantification for structured data analysis, presenting an incremental improvement with a novel reasoning mechanism.
The paper tackles the problem of quantifying implicit bias in structured datasets by developing a Fuzzy Cognitive Map model with a new reasoning mechanism that prevents neuron saturation and allows control over nonlinearity. The result includes analytical conditions for the existence and uniqueness of fixed-point attractors, demonstrating model convergence.
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.