Rebalancing the algorithm: pathways for mitigating bias in predictive policing

Abstract Crimes and accidents can have significant economic effects on not only people but also entire towns and countries. Predictive policing is a strategy that uses data analytics techniques, machine learning algorithms, and statistical models to predict the location and time of the likely events and individuals who might be involved in unlawful actions. Predictive policing uses data analytics, machine learning algorithms, and statistical modelling to support proactive policing and the allocation of operational resources. This paper examines the emergence of algorithmic bias in such systems and evaluates practical methods for its detection and mitigation. Using the Chicago Crime Dataset, the study applies three complementary techniques—data re-weighting, counterfactual analysis, and algorithm auditing—to identify both data-driven and model-driven sources of inequity. The results show that conventional predictive models disproportionately classify minority and historically over-policed neighbourhoods as high-risk, with risk estimates driven primarily by variables reflecting enforcement intensity rather than underlying crime incidence. Bias-controlled models produce materially different hotspot predictions and reduce measures of disparate impact, indicating that historical policing practices significantly shape risk forecasts. The findings highlight the need for transparency, fairness-aware modelling, and systematic auditing in the development and deployment of predictive policing systems. The paper presents an analysis of various techniques for diagnosing and mitigating bias in algorithmic crime prediction, contributing to a more accountable and evidence-based use of AI in law enforcement contexts.