An Empirical Evaluation of Bias Mitigation Techniques and Their Trade-offs with Predictive Performance in Machine Learning Models

Abstract

Machine learning models are increasingly applied in high-stakes domains, where concerns about fairness and accountability have become more critical. Although these models deliver strong predictive performance, they often inherit biases embedded in historical data, which can lead to unequal outcomes across different demographic groups. To address this issue, a variety of bias mitigation techniques have been introduced at different stages of the machine learning pipeline, including pre-processing methods that adjust the data, in-processing approaches that modify learning algorithms, and post-processing techniques that refine model outputs. However, improving fairness is not without challenges, as it may come at the expense of predictive accuracy, creating a trade-off that is not yet fully understood in practice. This study systematically evaluates the effectiveness of different bias mitigation strategies and examines their impact on model performance. Using benchmark datasets with sensitive attributes, baseline models are compared with models incorporating mitigation techniques. Performance is assessed using standard accuracy metrics, while fairness is measured through established statistical indicators. The findings reveal that some mitigation approaches enhance fairness but may reduce accuracy or affect model stability, while others improve generalization. Overall, the study provides valuable insights for selecting appropriate strategies in responsible machine learning applications.