Final report presenting the National Security Commission for Artificial Intelligence (NSCAI)'s recommendations for winning the AI era. It includes a 16 chapter Main Report and "Blueprints for Action that outline the concrete steps departments and agencies can take to implement NSCAI's recommendations." - Introduction.

This report describes an accountability framework for artificial intelligence (AI). The framework is organized around four complementary principles and describes key practices for federal agencies and other entities that are considering and implementing AI systems. Each practice includes a set of questions for entities, auditors, and third-party assessors to consider, along with audit procedures and types of evidence for auditors and third-party assessors to collect.

Report containing an overview of artificial intelligence (AI), recent growth in the field of AI, federal activity in AI (i.e., Executive Branch and Congress), and selected issues for congressional consideration to assist Congress in its work on AI.

Report detailing the activities of the United Nations regarding Artificial Intelligence (AI) toward their Sustainable Development Goals (SDGs).

This report details the creation of the Select Committee on Artificial Intelligence, chartered by action of the National Science and Technology Council (NSTC). The purpose of the Select Committee is to advise and assist the NSTC to improve the overall effectiveness and productivity of Federal research and development, demonstration, and education and workforce development efforts related to artificial intelligence.

This report provides background information on artificial intelligence and machine learning (AI/ML) and outlines a proposed framework to evaluate the safety of these technologies in consumer products. It outlines efforts underway and recommended future actions the CPSC can take to identify safety concerns with AI/ML technologies.

This report presents goals that will enable the Science and Technology Directorate to conduct artificial intelligence and machine learning research, development, test, and evaluation activities to support DHS mission needs.

This report discusses a proposed framework for modifications to AI/ML-based SaMD that is based on the internally harmonized International Medical Device Regulators Forum risk categorization principles, FDA's benfit-risk framework, risk management principles in the software modifications guidance, and the organization-based TPLC approach as envisioned in the Digital health Software Precertification Program. The authors ask for public feedback about the questions posed in the report.