Predictive Analytics for Crime Prevention: Ethical, Legal and Technical Challenges
Abstract
Predictive analytics has become an effective crime prevention tool as it allows law enforcement agencies to predict the future occurrence of a crime and invest resources before the fact; but its use presents a lot of theoretical, legal, and technical issues of bias, transparency, privacy, accountability. The paper outlines an ethical-legal-technical system of predictive crime analytics that encompasses data governance, biased modeling, explainable machine learning, compliance assessment, and decision-making by a human being into the predictive system. Empirical findings prove that the proposed methodology has a competitive predictive performance and a significant reduction of algorithm bias, an improvement in the interpretability and an increase in legal and ethical standards compared to traditional models. The results suggest the possibility of ensuring the effectiveness of crime prevention in a way that does not fully undermine the basic rights, democratic principles, and the confidence of the community and society, by means of responsible, governance-conscious predictive analytics.