The Impact of Artificial Intelligence on the Future of Workforces in the European Union and the United States of America (open access)

The Impact of Artificial Intelligence on the Future of Workforces in the European Union and the United States of America

Report on an economic study related to artificial intelligence: "This report is intended to highlight the economics behind AI-driven technological change with a particular focus on the institutional and policy decisions that will shape its future impact on the workforce. [...] Parts I and II of this report introduce AI and document its widespread adoption in the European Union and the United States; part III focuses on AI’s impact on labor; part IV contains case studies on hiring and logistics; part V concludes" (p. 3).
Date: December 2022
Creator: European Commission
System: The UNT Digital Library
Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning in Drug Development (open access)

Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning in Drug Development

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
Date: December 2019
Creator: United States. Government Accountability Office.
System: The UNT Digital Library