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 discussing currently available machine learning (ML) medical diagnostic technologies for five selected diseases, emerging ML medical diagnostic technologies, challenges affecting the development and adoption of ML technologies for medical diagnosis, and policy options to help address challenges in the use of artificial intelligence (AI) technologies in health care (Part One). Part Two presents a framework for evaluating and promoting provider adoption of new AI-assisted diagnostic decision support tools (AI-DDS), centered on four integrated domains: 1) Reason to Use, 2) Means to Use, 3) Methods to Use, and 4) Desire to Use.

Report representing the results of a Comptroller General forum on recent developments in the area of artificial intelligence (AI) - and key implications regarding potential benefits, challenges to realizing these benefits, and resulting policy implications and research priorities.

Statement discussing the evolution of artificial intelligence (AI) over time, the potential future opportunities and risks of AI, and the future implications of AI on policies and research priorities.

Artificial intelligence and machine learning (AI/ML) is a set of technologies that includes automated systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, and decision-making. AI/ML has promising applications in health care, including drug development. For example, it may have the potential to help identify new treatments, reduce failure rates in clinical trials, and generally result in a more efficient and effective drug development process. However, applying AI/ML technologies within the health care system also raises ethical, legal, economic, and social questions. GAO was asked to conduct a technology assessment on the use of AI technologies in drug development with an emphasis on foresight and policy implications. This report discusses (1) current and emerging AI technologies available for drug development and their potential benefits; (2) challenges to the development and adoption of these technologies; and (3) policy options to address challenges to the use of machine learning in drug development. -- from Foreword

Document summarizing deepfake technology and highlighting opportunities, challenges, and policy questions related to the development of this technology.