Feedback loops and bias in machine learning algorithms for predictive policing

Abstract

Predictive policing describes several emerging practices of implementing artificial intelligence and machine learning in police work, specifically in attempting to predict future crimes through algorithmic crime forecasting. These emerging practices have introduced many new opportunities for improved police work, but critics of predictive policing have raised both ethical and practical concerns. These concerns include the risk of feedback loops and bias. This thesis aims to contribute to this ongoing debate by examining how algorithmic crime forecasting tools produce bias and feedback loops and by exploring if it is possible to create algorithmic crime forecasting tools with reduced tendencies towards bias and feedback loops. Specifically, the focus is on the seminal and widely adopted PredPol system, which is based on an earthquake prediction system known as Epidemic Type Aftershock Sequence (ETAS). The methodology used in this studywas to replicate studies detailing the PredPol system, as well as studies criticising it. Based on previous findings by critics, a synthetic population and urn modelling was used to demonstrate the negative tendencies of the system. Based on this, an original framework was developed for evaluating modifications made to the algorithm by measuring the effectiveness in reducing feedback loop tendencies and improving fairness. This is done through metrics like the Predictive Accuracy Index (PAI), variations in the mean conditional intensity rates, λ, and the total fairness score, which evaluates the consistency of law enforcement attention across different demographic groups. To reduce the algorithm’s tendencies towards bias and feedback loops, a modified algorithm using rejection sampling and a fairness penalty was developed. While the proposed algorithmic adjustments lead to increased fairness and reduced feedback loop generation in predictive policing, they also introduce some trade-offs in predictive performance, particularly noted in the PAI values. However, the enhancements significantly mitigate biased policing practices and reduce the perpetuation of historical inequities, aligning more closely with ethical standards.