Utah S.B. 180 (Law Enforcement Usage of Artificial Intelligence)

• Authority: Utah
• Parent authority: State governments
• Jurisdiction: United States
• Collections: U.S. state and local documents
• Most recent activity: Enacted
• Most recent activity date: 2025-03-25

Risk subdomain coverage

Minimal coverage — Brief mention but no specific mitigations, recommendations or governance measures relating to the risk subdomain are described.

• False or misleading information
• Overreliance and unsafe use
• Lack of capability or robustness
• Lack of transparency or interpretability

All other risk subdomains had no coverage in this document.

Risk Coverage Summary

The document has minimal coverage of approximately 3-4 subdomains, with focus on misinformation risks (3.1), lack of transparency (7.4), and lack of robustness (7.3). Coverage is concentrated on AI system reliability and information quality concerns in law enforcement contexts.

Sector coverage

Good coverage — Comprehensive governance measures or mitigations with clear procedures relating to the sector are explicitly described.

• Public Administration (excl. National Security)

All other sectors had no coverage in this document.

Sectors Summary

This document exclusively governs the Public Administration sector, specifically law enforcement agencies. It establishes mandatory policies for generative AI use within law enforcement operations, including report generation and record-keeping activities.

Legislative Status

Hard Law

Legislative Status Summary

This is a binding state statute enacted by the Utah Legislature with mandatory requirements for law enforcement agencies, including specific policy requirements and certification obligations, with enforcement through administrative disciplinary action.

Actors

Proposer

• Entity Types: AI_Governance_Actor
• Entities: Legislature of the state of Utah
• Summary: The document explicitly states it was enacted by the Legislature of the state of Utah, which is the state legislative body that proposed and passed this law.

Target

• Entity Types: AI_Deployer
• Entities: law enforcement agencies in Utah; employees of law enforcement agencies
• Summary: The statute explicitly applies to law enforcement agencies and their employees who use generative AI in the course and scope of their work. Law enforcement agencies are deployers of AI technology in this context.

Enforcer

• Entity Types: AI_Governance_Actor
• Entities: head of the law enforcement agency
• Summary: The statute designates the head of each law enforcement agency as the enforcement authority who may impose administrative disciplinary action for policy violations.

Monitor

• Entity Types: AI_Governance_Actor
• Entities: head of the law enforcement agency
• Summary: While not explicitly stated as a monitoring role, the head of the law enforcement agency implicitly monitors compliance through their enforcement authority and oversight of policy implementation. The certification requirement also creates a monitoring mechanism through author attestation.

AI Lifecycle Stages

Lifecycle stages covered by the documents, as defined in the OECD Framework for the Classification of AI Systems

• Plan and Design
• Verify and Validate
• Deploy
• Operate and Monitor

AI Lifecycle Summary

The document focuses primarily on the Deploy and Operate and Monitor stages of the AI lifecycle. It addresses deployment through policy requirements for which AI technologies may be used and for what tasks, and emphasizes operational monitoring through mandatory review, certification, and disclaimer requirements for AI-generated content.

Technical Scope

Terms relating to technical scope that are mentioned or implicitly referenced in the document

• AI Systems
• Generative AI

Technical Scope Summary

The document explicitly defines and focuses on 'generative artificial intelligence' as a specific type of AI technology capable of creating content. It also provides a broader definition of 'artificial intelligence' but does not mention frontier AI, general purpose AI, foundation models, compute thresholds, or open-weight models.