  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.
