Renamed: Risk-based FAR Review & Decision Support Previously: Field Alert Reports (FAR) Prioritization Model
Description
Manual and subjective assessment process for Field Alert Reports (FARs) can lead to inconsistency in prioritizing and assessing reports, potentially leading to ineffective resource allocation where the same level of formality is being applied for all issues, regardless of risk.
Detailed example
AI-based machine learning classification of FAR risk into low, medium, and high; provides insights on problem clusters, rare-events, and source variables.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
Assist Field Alert Report (FAR) reviewers by providing objective intelligence and insights that help prioritize the highest risk reports, potentially reducing response time to high-risk issues while maintaining human oversight of all decisions and leading to Agency resources being used more efficiently.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
Internal Field Alert Reports data from LSMV