Police Use-of-Force Scoring for Policy and Peer Comparison
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Solution Overview
Problem
There is a need for a system that can evaluate whether a police officer's use of force in an incident is more or less than expected compared to other officers or department policies, and provide scores to rank an officer's use of force in relation to similar incidents.
Innovation Solution
A system that collects, stores, and processes data on use of force incidents using machine-learning to generate metrics and scores, analyzing the proportionality and escalation of force, and compares them to policies and peer group averages to identify potential adverse events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of use of force incidents is performed, then detailed analysis can be conducted, but the process is time-consuming and subjective
Solution Approach 1:
The patent replaces manual mechanical evaluation with an automated computer-based system that uses machine learning algorithms and data processing to objectively assess use of force incidents. The system automatically collects data from multiple sources, processes it through standardized algorithms, and generates scores without human intervention, thereby eliminating subjectivity and significantly reducing evaluation time while maintaining or improving accuracy.
Solution Approach 2:
The system enables self-service evaluation where the data collection and initial analysis are automatically performed by the system itself without requiring manual input for each incident. The automated architecture continuously processes incident data, compares it against established criteria, and generates evaluations autonomously, freeing human reviewers from routine assessment tasks.
2Measurement precision
If comprehensive data collection is implemented, then more accurate assessments can be made, but system complexity increases
Solution Approach 1:
The patent implements a universal data collection system that handles multiple data types and sources through a single integrated platform. The system is designed to collect, process, and analyze various forms of incident data (video, audio, text reports, sensor data) using the same architectural framework and processing algorithms, thereby achieving comprehensive data collection without proportionally increasing system complexity.
Solution Approach 2:
The system introduces standardized data processing intermediaries and abstraction layers that mediate between diverse data sources and the core analysis engine. These intermediaries normalize and standardize incoming data, allowing the system to handle comprehensive data collection while maintaining manageable complexity through modular architecture and standardized interfaces.
3Stability of the object's composition
If automated scoring system is implemented, then consistency is improved, but adaptability to unique incident contexts may be reduced
Solution Approach 1:
The patent implements a dynamic scoring system that adapts its evaluation criteria and weightings based on the specific context of each incident. Rather than applying rigid fixed rules, the system dynamically adjusts its analysis based on incident characteristics, officer credentials, subject behavior, and environmental factors, thereby maintaining consistency in its method while adapting to unique contextual situations.
Solution Approach 2:
The system changes evaluation parameters and criteria based on the specific incident context. Different incident types, severity levels, and situational factors trigger different parameter configurations in the scoring algorithm, allowing the automated system to maintain consistency in its approach while being highly adaptable to the unique aspects of each use of force incident.
Data Source
AI summary
A system and method for determining a score reflecting the use of force that a police officer applied in an incident, comprising a computer system for collecting, storing and processing data relating to an incident involving the police officer's use of force in one or more interactions within an incident; the computer system iteratively generating scores for each use of force interactions within the incident; the computer system comparing the police officer's use of force score, whether overall or for an interaction to a score for a group of officers; and, the computer system generating a risk score by running a model based on the use of force score.