Predictive Policing

Recent Developments in Law, Criminology, and Technology

Absztrakt

Predictive policing uses data-driven tools and algorithms to anticipate crime risks and help police allocate resources more effectively. Although recent advances in artificial intelligence have made these systems more sophisticated, growing research highlights important legal and ethical concerns. Models built on historical arrest data can unintentionally reproduce existing biases, creating feedback loops that disproportionately impact marginalized communities. These risks have intensified calls for greater transparency, accountability, and stronger legal safeguards. International responses differ widely. Several U.S. cities have already banned or restricted predictive policing due to civil rights concerns, while the European Union’s AI Act classifies such systems as “high-risk” and prohibits certain individual-level crime prediction models. Scholars increasingly stress the need to balance technological innovation with robust protections. Current research focuses on developing responsible frameworks – such as bias audits, human oversight, explainability, and community involvement – to ensure that predictive policing can enhance public safety without compromising fundamental rights.