Algorithmic fairness in predictive policing

Abstract The increasing use of algorithms in predictive policing has raised concerns regarding the potential amplification of societal biases. This study adopts a two-phase approach, encompassing a systematic review and the mitigation of age-related biases in predictive policing. Our systematic review identifies a variety of fairness strategies in existing literature, such as domain knowledge, likelihood function penalties, counterfactual reasoning, and demographic segmentation, with a primary focus on racial biases. However, this review also highlights significant gaps in addressing biases related to other protected attributes, including age, gender, and socio-economic status. Additionally, it is observed that police actions are a major contributor to model discrimination in predictive policing. To address these gaps, our empirical study focuses on mitigating age-related biases within the Chicago Police Department's Strategic Subject List (SSL) dataset used in predicting the risk of being involved in a shooting incident, either as a victim or an offender. We introduce Conditional Score Recalibration (CSR), a novel bias mitigation technique, alongside the established Class Balancing method. CSR involves reassessing and adjusting risk scores for individuals initially assigned moderately high-risk scores, categorizing them as low risk if they meet three criteria: no prior arrests for violent offenses, no previous arrests for narcotic offenses, and no involvement in shooting incidents. Our fairness assessment, utilizing metrics like Equality of Opportunity Difference, Average Odds Difference, and Demographic Parity, demonstrates that this approach significantly improves model fairness without sacrificing accuracy.