# Modeling Implicit Bias with Fuzzy Cognitive Maps

arXiv: https://arxiv.org/abs/2112.12713 · HTML: https://mlsanity.com/paper/2112.12713/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 43 references that are in the map.
- First posted on arXiv 23 Dec 2021.

## Builds on (most cited first, 12 of 12)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [A comparative study of fairness-enhancing interventions in machine learning](https://mlsanity.com/paper/1802.04422/) (2018)
- [Path-Specific Counterfactual Fairness](https://mlsanity.com/paper/1802.08139/) (2018)
- [Survey on Causal-based Machine Learning Fairness Notions](https://mlsanity.com/paper/2010.09553/) (2020)
- [Auditing Black-box Models for Indirect Influence](https://mlsanity.com/paper/1602.07043/) (2016)
- [Exposing the Probabilistic Causal Structure of Discrimination](https://mlsanity.com/paper/1510.00552/) (2015)
- [A fuzzy-rough uncertainty measure to discover bias encoded explicitly or implicitly in features of structured pattern classification datasets](https://mlsanity.com/paper/2108.09098/) (2021)
- [Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification](https://mlsanity.com/paper/2107.03423/) (2021)
- [Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning](https://mlsanity.com/paper/2005.06852/) (2020)

## Built on by (most cited first, 2 of 2)

- [Measuring Implicit Bias Using SHAP Feature Importance and Fuzzy Cognitive Maps](https://mlsanity.com/paper/2305.09399/) (2023)
- [On the Convergence of Sigmoid and tanh Fuzzy General Grey Cognitive Maps](https://mlsanity.com/paper/2409.05565/) (2024)

## Abstract (arXiv)

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.