MACO Project: Law Enforcement CAD Data Autocoder

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.

Start date

Not reported

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Law Enforcement

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

MACO is intended to solve the lack of standardization in Computer-Aided Dispatch (CAD) data across law enforcement agencies. CAD event descriptions are free-text, highly variable, and differ widely in terminology, structure, and coding practices. Because of this inconsistency, it is currently not possible to aggregate, compare, or analyze CAD data across jurisdictions at scale. MACO uses machine learning and language models to automatically classify raw CAD text into a standardized event taxonomy, enabling consistent analysis, cross-agency comparisons, and the development of national estimates of calls for service.

Expected benefits

The AI provides standardized classifications of CAD event text, allowing BJS and partner agencies to analyze police activity consistently across jurisdictions. For BJS, this enables the production of scalable, comparable national estimates of calls for service—filling a major data gap not addressed by traditional crime measures. For state and local agencies, the standardized schema improves internal organization of CAD data and supports regional or state-level comparisons of police workload and community needs. For the research community and the public, MACO expands understanding of how law enforcement resources are used, the types of events agencies respond to, and broader patterns of community demand for police services. Overall, the tool enhances data quality, improves analytic capacity, and supports evidence-based decision-making across the criminal justice ecosystem.

System outputs

The system outputs a standardized event-type classification for each CAD record. For each raw text description, the model generates a predicted category from a predefined event taxonomy (e.g., “Property Crime: Theft,” “Traffic Incident,” “Disturbance,” etc.). The final deliverable is a CAD dataset with these standardized classifications appended to each record, enabling consistent analysis and aggregation across agencies.

Technology

AI classification

Natural language processingSource: Natural Language Processing: AI that processes, interprets, and shares information in human language.

System name(s)

Not reported

Custom-developed code

Not reported

Public source code

Not reported

Sourcing

How it was built

Not reported

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported