Proposal of a Method for Identifying Unfairness in Machine Learning Models based on Counterfactual Explanations

Abstract

As machine learning models continue impacting diverse areas of society, the need to ensure fairness in decision-making becomes increasingly vital. Unfair outcomes resulting from biased data can have profound societal implications. This work proposes a method for identifying unfairness and mitigating biases in machine learning models based on counterfactual explanations. By analyzing the model’s equity implications after training, we provide insight into the potential of the method proposed to address equity issues. The findings of this study contribute to advancing the understanding of fairness assessment techniques, emphasizing the importance of post-training counterfactual approaches in ensuring fair decision-making processes in machine learning models.