A novel approach for assessing fairness in deployed machine learning algorithms.

ABSTRACT: Fairness in machine learning (ML) emerges as a critical concern as AI systems increasingly influence diverse aspects of society, from healthcare decisions to legal judgments. Many studies show evidence of unfair ML outcomes. However, the current body of literature lacks a statistically validated approach that can evaluate the fairness of a deployed ML algorithm against a dataset. A novel evaluation approach is introduced in this research based on k-fold cross-validation and statistical t-tests to assess the fairness of ML algorithms. This approach was exercised across five benchmark datasets using six classical ML algorithms. Considering four fair ML definitions guided by the current literature, our analysis showed that the same dataset generates a fair outcome for one ML algorithm but an

SUBMITTER: Uddin S 

PROVIDER: S-EPMC11291763 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature