Modeling Implicit Bias with Fuzzy Cognitive Maps

TL;DR

This paper introduces a Fuzzy Cognitive Map model to quantify implicit bias in datasets, using neural concepts and a novel reasoning mechanism with controlled nonlinearity to improve interpretability and convergence analysis.

Contribution

The paper proposes a new Fuzzy Cognitive Map approach with a normalization transfer function and provides analytical conditions for model convergence and fixed-point uniqueness.

Findings

Effective modeling of implicit bias in structured data.

A new reasoning mechanism with controlled nonlinearity.

Analytical conditions for convergence and fixed points.

Abstract

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.