Supercomputing for Safer Chemicals Act

• Authority: United States Congress
• Parent authority: Federal government
• Jurisdiction: United States
• Collections: U.S. federal laws
• Most recent activity: Defunct
• Most recent activity date: 2025-01-03

Risk subdomain coverage

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

• Governance failure
• 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 AI risk domains, with limited implicit coverage of approximately 2-3 subdomains. The primary focus is on using AI/ML for chemical safety assessment rather than addressing AI-specific risks. There is implicit coverage of system safety (7.3) through validation requirements and potential minimal coverage of governance (6.5) through regulatory framework establishment.

Sector coverage

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

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

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

• Agriculture, Mining, Construction & Manufacturing
• Health Care & Social Assistance

All other sectors had no coverage in this document.

Sectors Summary

The document primarily governs Scientific Research and Development Services through the establishment of a research consortium. It also has implications for Agriculture, Mining, Construction and Manufacturing sectors as the end-users of safer chemical alternatives, and Public Administration through EPA regulatory decision-making processes.

Legislative Status

Hard Law

Legislative Status Summary

This is a binding legislative act from the U.S. Congress with mandatory language, specific appropriations, and clear enforcement authority delegated to the EPA Administrator.

Actors

Proposer

• Entity Types: AI_Governance_Actor
• Entities: United States Congress; Senate and House of Representatives of the United States of America
• Summary: The document is a Congressional bill enacted by the Senate and House of Representatives, making Congress the proposing entity for this governance instrument.

Target

• Entity Types: AI_Developer, AI_Infrastructure_Provider
• Entities: National Laboratories of the Department of Energy; academic and other research institutions; Supercomputing for Safer Chemicals (SUPERSAFE) Consortium
• Summary: The Act targets entities that will develop and validate computational toxicology methods using AI, machine learning, and supercomputing. The Consortium members include National Laboratories, academic institutions, and research entities that will develop AI/ML models.

Enforcer

• Entity Types: AI_Governance_Actor
• Entities: Administrator of the Environmental Protection Agency; Environmental Protection Agency (EPA); Secretary of Health and Human Services; Secretary of Energy
• Summary: The EPA Administrator is designated as the primary enforcement authority with responsibility to establish and oversee the Consortium, in consultation with other federal agency heads.

Monitor

• Entity Types: AI_Governance_Actor
• Entities: Environmental Protection Agency; Administrator of the Environmental Protection Agency
• Summary: The EPA Administrator has implicit monitoring authority through oversight of the Consortium's activities and the requirement to make results publicly available, though explicit monitoring mechanisms are not detailed.

AI Lifecycle Stages

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

• Plan and Design
• Build and Use Model
• Verify and Validate
• Deploy
• Operate and Monitor

AI Lifecycle Summary

The document covers multiple AI lifecycle stages with primary focus on Build and Use Model (developing and validating computational toxicology models), Verify and Validate (testing predictability and reliability), and Deploy (making models available for regulatory use). It also addresses Plan and Design through establishing the consortium framework.

Technical Scope

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

• AI Models
• AI Systems
• Predictive AI
• Task-specific AI

Technical Scope Summary

The document explicitly mentions AI, machine learning, and computational models for toxicology prediction. It focuses on predictive AI applications rather than generative AI. No specific compute thresholds, foundation models, or open-source model provisions are mentioned.