Predicting Hate Crime Bias Using Machine Learning Based on Multidimensional Socio-demographic and Incident Features

Abstract

This study applies a Random Forest classifier to the FBI's national Uniform Crime Reporting (UCR) dataset containing 218,069 single-bias hate crimes from 1991to 2020. Within the dataset, crimes were predicted based on bias-driven motives: race, sexual orientation, gender, religion, or disability. Thirteen incident features were analyzed to form the prediction. The primary model chosen for this study oversampled the two smallest categories and tuned hyperparameters. Ultimately, an accuracy of 68.7% with F1 = 0.80 and recall = 0.93 was achieved for the main prediction target: racial bias. Five trials were conducted with different configurations of the train/test model, compared across two metrics. It can be seen that there is a significant trade-off between total balance within all 5 categories versus overall accuracy. It was determined the incident year was the dominant predictive feature, likely due to FBI changes in definition and classification across the 1991-2020 period. Results demonstrate the predictability of indicators within official records and are limited to UCR metrics of data collection.