AI Data Model Selection Through CMS Accuracy Tracking
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Solution Overview
Problem
Existing data models used by law enforcement agencies for predicting potential threats or events lack 100% accuracy, leading to incorrect assessments that can result in tragic outcomes for officers.
Innovation Solution
A Content Management System (CMS) is employed to continuously track, update, and improve data models by storing data model IDs, historical versions, accuracies, and underlying data sets, using a prediction model to select data models with high confidence levels and facilitate further improvements, ensuring accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data models are used for predicting potential threats or events, then predictive capabilities are provided to aid law enforcement operations, but accuracy cannot reach 100% leading to incorrect assessments
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring the performance and accuracy of data models through actual law enforcement operations. The CMS tracks prediction outcomes, compares them with actual results, and uses this feedback to identify models that need improvement or replacement, thereby systematically enhancing accuracy while managing complexity through automated performance evaluation.
Solution Approach 2:
The CMS enables self-service by automatically tracking, evaluating, and managing data model performance without requiring manual intervention for each assessment. The system autonomously monitors accuracy metrics, identifies underperforming models, and facilitates their replacement, reducing the complexity burden on operators while maintaining high reliability through continuous automated evaluation.
2Adaptability or versatility
If multiple data models are maintained for different applications, then versatility and adaptability are improved, but tracking and managing them becomes more complex
Solution Approach 1:
The CMS implements a universal tracking framework that handles multiple data models across different law enforcement applications through a single integrated system. This multi-functional platform manages diverse model types (threat prediction, event detection, pattern recognition) uniformly, reducing management complexity while preserving adaptability to various specialized applications through standardized interfaces and centralized control.
3Reliability
If data models are continuously updated and improved, then accuracy and reliability are enhanced, but time and resources for maintenance increase
Solution Approach 1:
The system performs preliminary actions by pre-establishing performance thresholds, evaluation criteria, and automated tracking mechanisms before data models are deployed. This advance preparation enables continuous monitoring and updating without requiring significant time investment during operations, as the framework is already in place to automatically evaluate and implement improvements when conditions warrant changes.
Data Source
AI summary
This disclosure describes techniques for continuous improvement of machine learning models (also called data models) in a Content Management System (CMS). In one example, a CMS may store a set of data models for each application such as plate number recognition, facial recognition, a determination of likelihood of assault to a law enforcement officer in a traffic violation or robbery scenario, and car identification. In an example embodiment, a predictive model may be used to select a data model from the plurality of data models. The selected data model may be further improved or trained to a new sample of data features to generate an output pattern (e.g., likelihood of assault to a law enforcement officer).