S.2455 Transparency and Responsibility for Artificial Intelligence Networks Act 2025

• Authority: United States Congress
• Parent authority: Federal government
• Jurisdiction: United States
• Collections: U.S. federal laws
• Most recent activity: Proposed
• Most recent activity date: 2025-07-04

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
• Economic and cultural devaluation of human effort

All other risk subdomains had no coverage in this document.

Risk Coverage Summary

The document has minimal coverage, focusing primarily on transparency requirements (7.4) with minimal coverage of privacy protections (2.1). The document establishes disclosure mechanisms to address lack of transparency in AI training data, with confidentiality protections for disclosed information. This is primarily a governance mechanism rather than a comprehensive risk mitigation framework.

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.

• Scientific Research & Development Services
• Public Administration (excl. National Security)

All other sectors had no coverage in this document.

Sectors Summary

This legislation primarily governs the Information sector where AI developers operate, specifically those engaged in data processing and AI model development. It creates cross-sectoral obligations applicable to any developer of generative AI models regardless of their primary industry, with particular implications for copyright-intensive sectors.

Legislative Status

Hard Law

Legislative Status Summary

This is a Congressional bill amending Title 17 of the United States Code, establishing a legally enforceable subpoena process with mandatory compliance requirements, sanctions for bad faith requests, and rebuttable presumptions for non-compliance. It uses mandatory language throughout and is governed by Federal Rules of Civil Procedure.

Actors

Proposer

• Entity Types: AI_Governance_Actor
• Summary: The document is a Congressional bill introduced in the U.S. Senate (S.2455) that amends Title 17 of the United States Code. It states 'Be it enacted by the Senate and House of Representatives of the United States of America in Congress assembled'.

Target

• Entity Types: AI_Developer
• Summary: The document explicitly defines and targets 'developers' of generative AI models, creating obligations for them to respond to subpoenas regarding training data. The definition includes those who design, code, produce, own or substantially modify generative AI models and engage in or supervise training dataset curation.

Enforcer

• Entity Types: AI_Governance_Actor
• Summary: Federal district courts are designated as the enforcement authority, with clerks issuing subpoenas and courts having power to impose sanctions for bad faith requests and address non-compliance through the Federal Rules of Civil Procedure.

Monitor

• Summary: No ongoing monitoring body is established. The mechanism is complaint-driven, with copyright owners initiating individual subpoena requests. Courts adjudicate disputes but do not conduct systematic monitoring of AI training practices.

AI Lifecycle Stages

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

• Collect and Process Data
• Build and Use Model

AI Lifecycle Summary

The document focuses primarily on the 'Collect and Process Data' and 'Build and Use Model' stages, as it establishes disclosure requirements for training material used to train generative AI models. It addresses the training phase of the AI lifecycle specifically, requiring developers to maintain and disclose records of copyrighted works used during model training.

Technical Scope

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

• AI Models
• AI Systems
• Generative AI

Technical Scope Summary

The document explicitly defines and focuses on 'artificial intelligence models' and 'generative artificial intelligence models' as its primary technical scope. It does not mention frontier AI, general purpose AI, foundation models, compute thresholds, or open-weight models. The focus is specifically on generative AI systems that create synthetic content.