Wasserstein-based fairness interpretability framework for machine learning models - Machine Learning

Contemporary machine learning (ML) techniques surpass traditional statistical methods in terms of their higher predictive power and their capability of processing a larger number of attributes. However, these novel ML algorithms generate models that have a complex structure which makes it difficult for their outputs to be interpreted with high precision. Another important issue is that a highly accurate predictive model might lack fairness by generating outputs that may result in discriminatory outcomes for protected subgroups. Thus, it is imperative to design predictive systems that are not only accurate but also achieve the desired fairness level. When used in certain contexts, predictive models, and strategies that rely on such models, are subject to laws and regulations that ensure fairness.