Forecasting and Criminal Justice Policy and Practice.

Area of Science:

• Criminology
• Public Policy
• Data Science

Background:

• Forecasting is increasingly utilized within criminal justice systems.
• Its application often influences operational decisions rather than policy formulation.
• Existing research lacks a comprehensive analysis of the organizational structures surrounding forecasting use.

Purpose of the Study:

• To examine the organization of criminal justice forecasting.
• To analyze the implications of these organizational structures for policymaking.
• To evaluate the transparency, reviewability, and consensual use of forecasting methods.

Main Methods:

• Case study analysis of predictive policing and prison population forecasting.
• Examination of organizational arrangements and historical development of forecasting methods.
• Assessment of transparency, data requirements, and community engagement.

Main Results:

• Forecasting is widespread in criminal justice, primarily informing practice, not policy.
• Prison population forecasting is integrated into budget processes, enhancing transparency.
• Predictive policing exhibits less transparency and limited community engagement, raising legitimacy concerns.

Conclusions:

• Organizational structures significantly impact the transparency and legitimacy of criminal justice forecasting.
• Greater transparency and community engagement are needed, particularly for predictive policing.
• Forecasting's potential for policy development remains largely untapped due to organizational constraints.