EADTC: An Approach to Interpretable and Accurate Crime Prediction

Machine learning applications related to high-stakes decisions are often surrounded by significant amounts of controversy. This has led to increasing interest in interpretable machine learning models. A well-known class of interpretable models is that of decision trees (DTs), which mirror a common strategy used by humans to arrive at solutions through a series of well-defined decisions. However, much of previous research on DTs for criminal justice predictions has focused primarily on collections (ensembles) of DTs whose results are aggregated together. Such DT ensembles are used to help improve accuracy; however, their increased complexity and deviation from human decision-making processes makes them much less interpretable compared to single-DT approaches. In this paper, we present a new DT model for criminal recidivism prediction that is designed with high interpretability, accuracy, and fairness as core objectives. The interpretability of the model stems from its formulation in terms of a single DT structure, while accuracy is achieved through an intensive optimization process of DT parameters that is carried out using a novel evolutionary algorithm. Through extensive experiments, we analyze the performance of our proposed EADTC (Evolutionary Algorithm Decision Tree for Crime prediction) method on relevant datasets. Our experiments show that the EADTC approach achieves competitive accuracy and fairness with respect to state-of-the-art ensemble DT models, while achieving higher interpretability due to the simpler, single-DT structure.