Dealing with Data Bias in Classification: Can Generated Data Ensure Representation and Fairness?

Fairness is a critical consideration in data analytics and knowledge discovery because biased data can perpetuate inequalities through further pipelines. In this paper, we propose a novel pre-processing method to address fairness issues in classification tasks by adding synthetic data points for more representativeness. Our approach utilizes a statistical model to generate new data points, which are evaluated for fairness using discrimination measures. These measures aim to quantify the disparities between demographic groups that may be induced by the bias in data. Our experimental results demonstrate that the proposed method effectively reduces bias for several machine learning classifiers without compromising prediction performance. Moreover, our method outperforms existing pre-processing methods on multiple datasets by Pareto-dominating them in terms of performance and fairness. Our findings suggest that our method can be a valuable tool for data analysts and knowledge discovery practitioners who seek to yield for fair, diverse, and representative data.