United States–Japan Research Exchange on Artificial Intelligence Proceedings from a Pair of Conferences on the Impact of Artificial Intelligence on Work, Health, and Data Privacy and on Disaster Prediction, Resilience, and Recovery SCOTT W. HAROLD, GREG BRUNELLE, RITIKA CHATURVEDI, JEFFREY W. HORNUNG, SHUNICHI KOSHIMURA, OSONDE A. OSOBA, CHIZURU SUGA Sponsored by the Government of Japan SOCIAL AND ECONOMIC WELL-BEING For more information on this publication, visit www.rand.org/t/CFA521-1 Published by the RAND Corporation, Santa Monica, Calif. © Copyright 2020 RAND Corporation R® is a registered trademark. Limited Print and Electronic Distribution Rights This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited. Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial use. For information on reprint and linking permissions, please visit www.rand.org/pubs/permissions. The RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest. RAND’s publications do not necessarily reflect the opinions of its research clients and sponsors. Support RAND Make a tax-deductible charitable contribution at www.rand.org/giving/contribute www.rand.org Preface Artificial intelligence (AI) and machine learning (ML—hereafter referred to as AI/ML) are being applied more and more widely across the globe, affecting how individuals work, pursue health, and protect their communities. These changes have implications for society, the econ- omy, and data science. The experiences of the United States and Japan, two of the world’s wealthiest and most technologically advanced liberal democracies, represent important leading indicators of how AI/ML affect human society now and will continue to do so. U.S. and Japa- nese experiences also carry lessons for each other about how the two sides of the Pacific might think about the policy impacts that AI/ML technologies can have and what policies might be necessary to effectively and safely employ learning algorithms. How are these two advanced countries thinking about and employing AI? What can they learn from each other’s experi- ences and approaches? And are there areas for cooperation that might be pursued? Given the importance of understanding the implications of AI/ML, the RAND Corpora- tion convened a pair of public conferences at in Pittsburgh, Pennsylvania, and New Orleans, Louisiana, that brought together leading U.S. and Japanese experts on work, health, and data security (Conference I), and on international affairs, disaster response, and disaster modeling (Conference II) to exchange views on AI/ML technologies. At the first conference, on October 10, 2019, noted AI ethicist David Danks (L.L. Thurstone Professor of Philosophy and Psychol- ogy and Head of the Department of Philosophy at Carnegie Mellon University) gave a keynote speech in which he discussed decisionmaking and biases in AI and the concerns that these issues raise as they are employed on a widening scale. At the second conference, on February 18, 2020, Charles Sutcliffe (Chief Resilience Officer, Governor’s Office of Coastal Activities) explained the challenges that Louisiana faces in protecting and preserving coastal wetlands and how the state employs a variety of forecasting and modeling technologies in an attempt to prevent erosion and minimize damaging weather events in an era of accelerating global warming and climate change. These conference proceedings capture insights from the two conferences, recount some of the exchanges among the participants at those sessions, and are built around the papers that the conference presenters submitted after the conferences concluded. The views articulated and expressed herein, including those about any technology or commercial product, are those of the participants and should not be construed as an endorsement by RAND or its research sponsors. This effort was sponsored by the Government of Japan and conducted in the Community Health and Environmental Policy Program within RAND’s Social and Economic Well-Being Division. RAND Social and Economic Well-Being is a division of the RAND Corporation that seeks to actively improve the health and social and economic well-being of populations and communities throughout the world. The Community Health and Environmental Policy Program focuses on such topics as infrastructure, science and technology, community design, iii iv United States–Japan Research Exchange on Artificial Intelligence community health promotion, migration and population dynamics, transportation, energy, and climate and the environment, as well as other policy concerns that are influenced by the natural and built environment, technology, and community organizations and institutions that affect well-being. For more information, email [email protected]. Contents Preface ........................................................................................................... iii Figures ...........................................................................................................vii Abbreviations ................................................................................................... ix CHAPTER ONE Introduction ..................................................................................................... 1 A Brief History of Artificial Intelligence ...................................................................... 3 AI for Social Good ............................................................................................... 5 Chapter Summaries .............................................................................................. 8 CHAPTER TWO AI and Labor Automation: A Cross-Cultural Assessment .............................................11 A More Careful Examination .................................................................................12 A Task-Based Reframing .......................................................................................13 Other Modes of Occupational Impact .......................................................................14 Differential Population Effects ................................................................................14 The United States and Japan: The Role of Culture in Explaining Reactions to AI.....................15 Potential Attitude Attributions and Further Questions ....................................................17 Conclusion .......................................................................................................18 CHAPTER THREE Artificial Intelligence, Big Data, and Precision Medicine .............................................19 Many Terms, One Overarching Goal ........................................................................19 Personalized Medicine ......................................................................................... 20 Precision Medicine ............................................................................................. 20 Precision (Public) Health .......................................................................................21 Key Drivers of Precision Medicine ........................................................................... 22 The Need for New Methods .................................................................................. 22 Data Explosion ................................................................................................. 23 New Methods, Large-Scale Data, and Advanced Analytics Could Fundamentally Revolutionize the Study and Delivery of Health Care ................................................25 Key Policy Considerations Required to Enable Success of Precision Medicine and Health .......... 26 U.S., Japanese Experiences Show Precision Medicine Requires Policy Support to Fully Maximize Technology Solutions ....................................................................... 27 Conclusion ...................................................................................................... 28 v vi United States–Japan Research Exchange on Artificial Intelligence CHAPTER FOUR Artificial Intelligence and the Problem of Data Governance: Building Trust in Data Flows for the Benefit of Society ........................................................................29 Japan’s Proposed Information Management Solution ..................................................... 30 Are Such Concepts Workable? .................................................................................32 Conclusion ...................................................................................................... 34 CHAPTER FIVE Building Resilience: AI/ML Technologies and Natural Disaster Risk Assessment ...............35 Existing Modeling Solutions: Data and Technology ...................................................... 36 Emergency Management and Data Analytics .............................................................. 36 One Concern Solutions: AI/ML for Disaster Resilience ...................................................41 Comprehensive Resilience: Measuring