Interpreting Criminal Charge Prediction and Its Algorithmic Bias via Quantum-Inspired Complex Valued Networks

TL;DR

This paper introduces a quantum-inspired complex-valued neural network approach to predict criminal charges, providing transparency and addressing bias by analyzing feature importance and temporal behavior patterns over twenty years.

Contribution

It proposes a novel quantum-inspired complex-valued network for criminal charge prediction that enhances interpretability and reduces bias compared to traditional deep learning models.

Findings

High prediction accuracy and reliability on real data

Criminal history significantly influences predictions

Race and age are statistically insignificant factors

Abstract

While predictive policing has become increasingly common in assisting with decisions in the criminal justice system, the use of these results is still controversial. Some software based on deep learning lacks accuracy (e.g., in F-1), and importantly many decision processes are not transparent, causing doubt about decision bias, such as perceived racial and age disparities. This paper addresses bias issues with post-hoc explanations to provide a trustable prediction of whether a person will receive future criminal charges given one's previous criminal records by learning temporal behavior patterns over twenty years. Bi-LSTM relieves the vanishing gradient problem, attentional mechanisms allow learning and interpretation of feature importance, and complex-valued networks inspired quantum physics to facilitate a certain level of transparency in modeling the decision process. Our approach shows a consistent and reliable prediction precision and recall on a real-life dataset. Our analysis of the importance of each input feature shows the critical causal impact on decision-making, suggesting that criminal histories are statistically significant factors, while identifiers, such as race and age, are not. Finally, our algorithm indicates that a suspect tends to rather than suddenly increase crime severity level over time gradually.