Improving the Fairness of Deep-Learning, Short-term Crime Prediction with Under-reporting-aware Models

TL;DR

This paper introduces a novel deep learning model that combines pre-processing and in-processing fairness techniques to improve the fairness of short-term crime prediction, especially across minority groups, while acknowledging a trade-off with accuracy.

Contribution

The paper proposes a new deep learning architecture that integrates bias mitigation methods to enhance prediction fairness in crime forecasting.

Findings

Improved fairness in crime predictions across minority groups.

Trade-off observed: increased fairness reduces overall accuracy.

Outperforms existing in-processing de-biasing approaches.

Abstract

Deep learning crime predictive tools use past crime data and additional behavioral datasets to forecast future crimes. Nevertheless, these tools have been shown to suffer from unfair predictions across minority racial and ethnic groups. Current approaches to address this unfairness generally propose either pre-processing methods that mitigate the bias in the training datasets by applying corrections to crime counts based on domain knowledge or in-processing methods that are implemented as fairness regularizers to optimize for both accuracy and fairness. In this paper, we propose a novel deep learning architecture that combines the power of these two approaches to increase prediction fairness. Our results show that the proposed model improves the fairness of crime predictions when compared to models with in-processing de-biasing approaches and with models without any type of bias correction, albeit at the cost of reducing accuracy.