FairGridSearch: A Framework to Compare Fairness-Enhancing Models
Shih-Chi Ma,
Tatiana Ermakova,
Benjamin Fabian
Abstract:Machine learning models are increasingly used in critical decision-making applications. However, these models are susceptible to replicating or even amplifying bias present in real-world data. While there are various bias mitigation methods and base estimators in the literature, selecting the optimal model for a specific application remains challenging. This paper focuses on binary classification and proposes FairGridSearch, a novel framework for comparing fairness-enhancing models. FairGridSearch enables expe… Show more
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