Classifying Hate: Legal and Ethical Evaluations of ML-Assisted Hate Crime Classification and Estimation in Sweden
Hate crimes, driven by biases against specific demographic groups, harm not only individuals but undermine the security, trust, and cohesion of entire communities. Accurately identifying such crimes remains a significant challenge due to under-reporting, limited training, and the complexity of determining bias motivations. In this paper, we analyze the results of a text classification model developed to improve the precision of hate crime statistics and identification in Sweden. Empirical results indicate the model outperforms traditional manual police classification of hate crimes, achieving higher precision across various crime types and regions. We further disaggregate performance to pinpoint persistent challenges and highlight categories where both human and machine decision-makers struggle. While the model focuses on statistical estimation rather than direct case-level decision-making, we discuss the broader implications of algorithmic transparency, accountability, and explainability. Ultimately, this research illustrates how transformer-based neural networks can responsibly bolster the detection and understanding of hate crimes, informing policies to better protect vulnerable communities.
Collaboration
The project is in collaboration with Holli sargeant at University of Cambridge and the National Council for Crime Prevention.
Project members at the department