Balancing Fairness and Accuracy for Predictive Models in Criminal Justice Applications Using Multi-Objective Optimization Methods

In the field of predictive modeling for criminal justice applications, the dual challenges of ensuring fairness and maintaining interpretability are crucial. This paper addresses these challenges by introducing a new approach to optimizing decision trees using evolutionary algorithms (EAs). Our approach focuses on refining decision trees to achieve a balance between accuracy and algorithmic fairness, a task complicated by potential bias present in historical data. By leveraging the principles of multi-objective optimization, our model systematically trades off prediction accuracy and fair-ness. The evolutionary process characterized by selection, crossover, and mutation is tailored to fit the decision tree structure, ensuring that model development is not only accurate but also promoting measurable fairness. Experimental results demonstrate the effectiveness of our approach in providing interpretable and fair predictive models that can be considered for high-stakes applications in criminal justice. More broadly, this research makes a significant contribution to the field of explainable machine learning, providing a powerful framework for engineering systems that are transparent, fair, and adaptable to different data environments.