Washington HB 2503 (Regulating artificial intelligence training data.)

• Authority: Washington
• Parent authority: State governments
• Jurisdiction: United States
• Collections: U.S. state and local documents
• Most recent activity: Proposed
• Most recent activity date: 2026-01-30

Risk subdomain coverage

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

• Lack of transparency or interpretability

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

• Compromise of privacy
• AI system security vulnerabilities

All other risk subdomains had no coverage in this document.

Risk Coverage Summary

The document has focused coverage primarily on transparency and interpretability (7.4), with minimal coverage of privacy concerns (2.1) and security vulnerabilities (2.2). The bill's primary purpose is to address lack of transparency in generative AI training data through mandatory disclosure requirements.

Sector coverage

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

• Information

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

• Trade, Transportation & Utilities
• Scientific Research & Development Services
• Health Care & Social Assistance
• Public Administration (excl. National Security)
• National Security

All other sectors had no coverage in this document.

Sectors Summary

This legislation applies broadly across all sectors where developers create generative AI systems for commercial distribution or public use. The Information sector is most directly implicated as it includes AI/software developers. However, specific exemptions exist for systems used in aircraft operations (Transportation), national security/military/defense purposes, and FDA-regulated medical devices (Healthcare).

Legislative Status

Hard Law

Legislative Status Summary

This is a binding legislative act from the Washington State Legislature with mandatory disclosure requirements and enforcement through the state's Consumer Protection Act, including penalties for non-compliance.

Actors

Proposer

• Entity Types: AI_Governance_Actor
• Entities: Washington State Legislature
• Summary: The document is a legislative act proposed and enacted by the Washington State Legislature, as indicated in the opening enactment clause.

Target

• Entity Types: AI_Developer
• Summary: The law explicitly targets developers of generative AI systems intended for commercial distribution or public use. The definition excludes entities developing AI solely for internal use, research purposes, public entities, and tribal nations.

Enforcer

• Entity Types: AI_Governance_Actor
• Entities: Washington State Consumer Protection authorities (implied through reference to Chapter 19.86 RCW)
• Summary: The document classifies violations as unfair trade practices under Washington's Consumer Protection Act, which implies enforcement by the state's consumer protection authorities, though no specific agency is named.

Monitor

• Summary: The document does not explicitly specify any monitoring body or oversight mechanism. The primary compliance mechanism is public disclosure on developers' websites, which enables public scrutiny but no formal monitoring entity is designated.

AI Lifecycle Stages

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

• Plan and Design
• Collect and Process Data
• Build and Use Model
• Verify and Validate
• Deploy

AI Lifecycle Summary

The document primarily focuses on the data collection/processing stage and model building stage, requiring comprehensive disclosure of training data characteristics. It also addresses deployment by requiring disclosure before systems are made publicly available. The emphasis is on transparency throughout the development lifecycle, from data collection through to public release.

Technical Scope

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

• AI Models
• Foundation Models
• AI Systems
• Generative AI

Technical Scope Summary

The document explicitly defines and covers artificial intelligence systems and generative artificial intelligence systems in detail. It defines AI as machine learning systems using data to train statistical models, and generative AI as systems that generate novel data based on foundation models. The document does not reference frontier AI, general-purpose AI, compute thresholds, or open-weight models, but does address synthetic data generation.