Chairman Babin Applauds Committee Passage of Bipartisan AI Legislation

(Washington, D.C.) - Today, House Science, Space, and Technology Committee Chairman Brian Babin released the following statement after the Committee passed a bipartisan package of artificial intelligence legislation, including the AI-Ready Federal Data Guidelines Act (H.R. 9341), AI Security and Innovation Act (H.R. 9363), CREATE AI Act (H.R. 2385), NSF AI Education Act of 2025 (H.R. 5351), LIFT AI Act (H.R. 5584), READ AI Models Act (H.R. 6461), Protecting Consumers from Deceptive AI Act (H.R. 8893), AI Flaw Reporting and Security Enhancement Act (H.R. 9333), Workforce for AI Trust Act (H.R. 9334), and Data Infrastructure Energy Measurement and Standards Act (H.R. 9372).

"Today, our Committee took another important step toward securing America's leadership in artificial intelligence," said Chairman Babin. "This bipartisan package expands access to cutting-edge research, strengthens our AI workforce, promotes responsible innovation, and bolsters our national security. I appreciate my colleagues for their work on these bills, and I look forward to advancing them through the legislative process."

Highlights of H.R. 9341:

• Directs NIST to develop voluntary guidelines to help federal agencies prepare datasets for training artificial intelligence models.
• Improves the quality and accessibility of federal data to support American AI innovation and strengthen U.S. competitiveness.
• Launches pilot programs to develop AI-ready data guidelines for strategic sectors, including biotechnology and biomanufacturing.

Highlights of H.R. 9363:

• Establishes the Center for AI Security and Innovation at NIST to evaluate AI-related national and economic security risks while supporting continued American leadership in AI research and development.
• Strengthens voluntary collaboration between government and industry to identify emerging AI security risks, improve testing and evaluation, and support the development of voluntary standards and best practices.
• Preserves NIST's nonregulatory role by prohibiting the Center from exercising regulatory, rulemaking, or enforcement authority while improving coordination on AI security.

Highlights of H.R. 2385:

• Establishes the National Artificial Intelligence Research Resource (NAIRR) to expand access to computing power, datasets, software, and other resources needed for AI research and development.
• Expands access to AI research infrastructure for researchers, educators, students, small businesses, and federal partners to help accelerate American innovation.
• Strengthens public-private partnerships by leveraging the expertise and resources of federal agencies, universities, nonprofit organizations, and industry to support AI research and workforce development.

Highlights of H.R. 5351:

• Authorizes NSF scholarships, fellowships, and professional development programs to prepare students, educators, and workers for careers in artificial intelligence.
• Supports research on AI education to improve instructional tools, teaching methods, and classroom integration across K-12 and higher education.
• Encourages collaboration among educators, researchers, and industry to strengthen AI education and build the workforce needed to support America's future competitiveness.

Highlights of H.R. 5584:

• Promotes AI literacy in K-12 education by supporting the development of age-appropriate instructional materials and resources for students and educators.
• Equips educators with AI knowledge and tools through teacher training and professional development to support effective classroom instruction.
• Helps prepare the next generation of innovators by expanding foundational AI education and strengthening America's future workforce.

Highlights of H.R. 6461:

• Establishes a NIST pilot program to develop voluntary resources for documenting AI models, including standardized templates and technical guidance.
• Promotes greater transparency by improving how AI models are documented, evaluated, and understood across sectors.
• Supports informed AI adoption by providing developers and users with consistent resources to assess AI models and their intended uses.

Highlights of H.R. 8893:

• Directs NIST to advance research on AI-generated content detection to support technologies that can detect, authenticate, and disclose the provenance of digital content.
• Develops voluntary standards and best practices to help identify and label AI-generated or AI-manipulated content across text, audio, video, and other digital formats.
• Brings together industry, academia, and government stakeholders to support the development of technical guidance that helps consumers distinguish authentic digital content from AI-generated material.

Highlights of H.R. 9333:

• Creates a voluntary AI flaw reporting program at NIST, in consultation with CISA, to collect, track, and better understand AI vulnerabilities, failures, and security incidents.
• Develops common definitions and reporting frameworks to improve consistency in identifying, classifying, and communicating AI safety and security issues across sectors.
• Supports collaboration among government, industry, and academia to strengthen AI security, improve information sharing, and promote voluntary best practices for addressing AI flaws.

Highlights of H.R. 9334:

• Supports interdisciplinary AI fellowships and skills-based training through NSF to help prepare the next generation of researchers and practitioners working on trustworthy AI.
• Expands NIST's AI workforce activities by directing the agency to develop a national framework identifying the knowledge, skills, and tasks needed for AI-related careers.
• Promotes collaboration across government, academia, and industry to strengthen the workforce needed to develop, evaluate, and govern safe and trustworthy AI systems.

Highlights of H.R. 9372:

• Develops best practices for measuring data center energy and water use to improve consistency, transparency, and data quality across the sector.
• Improves energy demand forecasting by directing NIST and DOE to study data availability and identify opportunities to enhance forecasting capabilities.
• Supports voluntary standards and information sharing by promoting standardized metrics, collaboration with stakeholders, and the development of internationally recognized measurement standards.

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