Automated Data Bias Mitigation Technique for Algorithmic Fairness

Machine learning fairness enhancement methods based on data bias correction are usually divided into two processes: The determination of sensitive attributes (such as race and gender) and the correction of data bias. In terms of determining sensitive attributes, existing studies tend to rely too heavily on sociological knowledge and neglect the importance of exploring potential sensitive attributes directly from the data itself. The accuracy of this approach is limited when dealing with data that cannot be fully explained by sociological factors. Regarding data bias correction, existing methods are primarily categorized into causality-based and association-based methods. The former requires a deep understanding of the underlying causal structure in the dataset, which is often difficult to achieve in practice. The latter method correlates sensitive attributes with algorithmic results through statistical measures, but this approach often tends to ignore the impact of sensitive attributes on other attributes. In this paper, we formalize the identification of sensitive attributes as a problem solvable through data analysis, without relying on commonly recognized knowledge in social science. We also propose a data pre-processing method that considers the effects of attributes correlated with sensitive attributes to enhance algorithmic fairness by combining the association-based bias reduction method. We evaluated our proposed method on a public dataset. The evaluation results indicate that our method can accurately identify sensitive attributes and improve the fairness of machine learning algorithms compared to existing methods.