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LEVI TILLEMANN AND COLIN MCCORMICK ROADMAPPING A U.S.-GERMAN AGENDA FOR ARTIFICIAL INTELLIGENCE POLICY MARCH 2017 About the Authors Acknowledgements Dr. Levi Tillemann is Managing Partner at Valence Strategic and a fellow at New America. Dr. Tillemann is also the author of The Great Race: The Global Quest for the Car of the Future (Simon & Schuster 2015) a book on the intersection of policy and innovation in the global auto industry. His current research focus is on robotics and AI — with an emphasis on self-driving cars and transportation. Previously he served as Special Advisor for Policy and International Affairs at the U.S. Department of Energy and was CEO of IRIS Engines, a company he founded to develop a smaller, more efficient and more powerful combustion engine (based on a design for which he holds multiple patents). Tillemann was also an associate director at IHS CERA. He has published in both academic journals and general interest publications including the Washington Post, the Wall Street Journal, Fortune, The New Yorker and is a regular guest on radio and TV programs including NPR’s Diane Rehm Show and Marketplace. He is proficient in Chinese, Japanese, Spanish and Portuguese. Tillemann is a graduate of Yale University, was a Fulbright scholar in Taiwan and China, and has a Ph.D. in Japan and China studies from Johns Hopkins School of Advanced International Studies. New America would like to thank the German Embassy in Washington, D.C. for sponsoring the workshop “Roadmapping a US-German Agenda for Artificial Intelligence Policy.” In addition to helping conceptualize this workshop, embassy officials helped bring together a group of world-class experts in robotics, machine learning and manufacturing as well as industry representatives and officials from various government ministries, industry organizations and think tanks. The resulting discussion pointed toward a rich landscape for future inquiry and many opportunities and challenges that lie ahead as the United States and Germany embrace a new generation of information technologies and robotics. New America would also like to acknowledge Valence Strategic for its role in conceptualizing and executing the workshop and aggregating the resulting policy research agenda. Dr. Colin McCormick is Partner and Chief Technologist at Valence Strategic. As Senior Advisor for R&D at the U.S. Department of Energy McCormick served as primary science advisor to the U.S. Department of Energy for energy innovation from 2010 to 2013. He has an extensive background in technology analysis in energy, robotics, and computer vision. His current research focus is on the implications of artificial intelligence in a range of fields, particularly transportation and satellite imagery analysis. He also conducts computer vision research relating to change detection in aerial imagery. McCormick has published in a range of venues including both academic journals and general interest publications such as Fortune, Slate, and The New Yorker. Previously, he was a professional staff member for the House Science & Technology Committee. McCormick has a Ph.D. in atomic and optical physics from UC Berkeley and conducted post-doctoral research in quantum optics at the National Institute of Standards and Technology (NIST) in the group of 1997 Physics Nobel Laureate Bill Phillips. About New America New America is committed to renewing American politics, prosperity, and purpose in the Digital Age. We generate big ideas, bridge the gap between technology and policy, and curate broad public conversation. We combine the best of a policy research institute, technology laboratory, public forum, media platform, and a venture capital fund for ideas. We are a distinctive community of thinkers, writers, researchers, technologists, and community activists who believe deeply in the possibility of American renewal. Find out more at newamerica.org/our-story. Contents Introduction 2 Safety (Physical Safety, Cybersecurity, Etc.) 3 Legal/Policy 4 Privacy/Data Sharing 5 Environment 6 Ethics 7 Distribution (Labor, Wages, Distribution, Taxation, Welfare, Etc.) 8 Notes 10 INTRODUCTION 2 Artificial Intelligence (AI) is poised to transform society with impacts as far-reaching and profound as electricity or the internal combustion engine. Policymakers have a limited window within which to grapple with foundational policy challenges posed by the first general purpose technology of the 21st century. This task is complicated by the fact that AI will impact almost every facet of our economy and society—including areas as diverse as financial planning, policing, elections, manufacturing, and transportation. into new AI-based frameworks. The utilization of AIbased technologies will also differ based on factors ranging from demographics to economic structure. AI’s role in our economy and society will cross borders. It is therefore necessary to advance a policy discussion that extends beyond individual countries’ domestic policy concerns. This policy research roadmap examines these cross-border dynamics in the context of the U.S.German technology, trade, social, and economic relationship. Both the U.S. and Germany are highly industrialized countries striving to understand the impact of digitalization and new forms of data analytics. The emergence of AI as an integral part of the twenty-first century economy will no doubt have significant implications for the U.S.-German cultural, political and economic relationship. Policymakers in both countries will confront these challenges within distinct social, cultural, and legal settings. Under U.S. and German law there will be differing rules and expectations regarding aggregation, storage, and utilization of data feeding To better understand areas in which transatlantic policymakers can collaborate on understanding key policy issues in the field of AI, New America and the German Embassy in Washington, D.C. held a joint workshop to roadmap critical research areas related to artificial intelligence. The workshop brought together a diverse range of participants from academia, government, industry representatives, and think tanks to discuss questions related to the policy, social, and technological implications of AI across society. The scope was policies that provide a framework to enable socially beneficial uses of AI between now and 2035. This policy research roadmap examines cross-border dynamics in the context of the U.S.-German technology, trade, social,and economic relationship. Some of the key insights and questions highlighted by participants are summarized below. NEW AMERICA SAFETY (PHYSICAL SAFETY, CYBERSECURITY, ETC.) Allowing computers to make decisions that would previously have been overseen by human beings poses a number of challenges—none more fundamental than ensuring basic safety. Not only does AI impact the security of communications and financial systems, but integration between AI and robotics will increasingly lead to new safety challenges in the physical world. Some key topics and questions for research are included below. Are there any areas/activities in which AI should be prohibited or restricted? There are precedents for emerging technologies that pose particular dangers to be restricted, including nuclear/chemical/biological weapons and human cloning. Does AI pose dangers in any specific areas that would justify such restrictions?2 Can we create fail-safe safety mechanisms for AI? How do we classify the threat levels associated with various AI technologies? What approach should we use when trying to understand the relative dangers/threat levels associated with AI? Should threats be separated into physical, financial, privacy, and other categories? Will it be sufficient to extend existing cybersecurity risk assessment frameworks (such as the NIST approach to Industrial Control System Security1) or is an entirely new framework required? An example of a similar schema is the U.S. Department of Transportation automation levels for autonomous vehicles or the “Strategy for Autonomous and Connected Driving” of the German Federal Government. Air gaps provide a physical mechanism to isolate sensitive information technology systems from hacking. Would similar hardware solutions be effective for ensuring the safety of AI and intelligent robotics (e.g. physical dongles)? If so, should they be adopted as best practice? How should geo-fencing be utilized by regulators? As a response to airspace restrictions, some drone manufacturers have implemented “geo-fencing,” such that their products cannot operate in certain geographical locations. Is this a viable safety measure for other forms of AI that control movable systems (cars, trucks, etc.)? How reliable can geofenced systems be made in practice? (See Sen. Roadmapping a U.S.-German Agenda for Artificial Intelligence Policy 3 Schumer’s support for Federal Aviation Agency (FAA) efforts to mandate geo-fencing of aerial drones.)3 Can the United States and Germany develop a common agenda around testing and validation for autonomous robots? Autonomous vehicles are one example of an emerging AI-governed robotics technology that will need to be regulated. How can the United States and Germany collaborate on setting standards for this and other AI-enabled robotics technologies as they arise? What would harmonized testing methods be based on?4 One potential platform for collaboration is Germany’s “Digital Testbed Autobahn,” which the German federal Government has established on the A9 autobahn as a testing ground for autonomous vehicles and other emerging technologies. LEGAL/POLICY Legal frameworks that govern commerce, criminal law and liability in and between advanced economies have developed over the course of millennia and are enormously complex. These rules generally assume the presence of a human being who can be held responsible for actions that impact society. The advent of AI-based decision-making poses a host of questions for this approach to law and policy including the following: How do the distinct approaches to technological deployment and development affect U.S. and German needs for AI regulation? In the United States there appears to be a decided preference toward first implementing emerging technologies and then developing regulatory frameworks for them. In Germany, regulation often precedes implementation. What are the implications of this difference for industry development? 4 What are the different challenges associated with the industrial structures of Germany and the United States? Germany’s heavy economic emphasis on manufacturing implies that its focus should be on preparing industries and workers for incorporating AI into existing manufacturing systems. In the United States, large-scale deindustrialization implies the possibility of recapturing jobs from low wage countries. In some cases such reindustrialization is occurring in Germany as well (e.g. creation of local production sites by Adidas). How can we use existing regulatory function to encourage positive path dependencies? Like politics, the ultimate shape of a technology is often dictated by historical chance. Historians and political scientists call these developments “path dependences.” Are there positive path dependencies NEW AMERICA in AI that we can encourage through existing institutional structures? is desirable that countries establish standards that promote shared values, economic, and social goals. Do we require new regulatory structures to deal with regulating AI-enabled industries? Compare U.S. and German standards in autonomous vehicles Existing regulatory frameworks were not designed to deal with the advent of AI. For instance, decisions made by AI systems are difficult to trace back to individuals, making responsibility for the outcomes unclear. AI’s technical complexity also makes it imperative that regulatory institutions have sufficient technical capacity to understand key issues. What are the possible avenues for addressing this shortfall in institutional capacity? Do we need new institutions?5 In developed countries, mass deployment of autonomous vehicles is likely to commence within a decade. Develop a simple comparison of regulatory and safety standards between U.S. and German (EU, Japanese, other) autonomous vehicle regulations. How to engage multi-country coalitions for AI development? What is the relationship between trade and standards for AI? AI technical standards have the potential to be deployed as a barrier to various forms of trade. Many of the concerns surrounding AI and trade may be legitimate. However, others may be excuses for various forms of protectionism. AI will have profound implications for safety, ethics, environment, and distribution of wealth. It PRIVACY/DATA SHARING In a hyper-connected world, the ubiquity of data presents opportunities to build a society that is more just, efficient, and human centered. Simultaneously, there is the potential for this data to be misused. In many cases the ethics of a particular use of data may be ambiguous. Questions surrounding AI and data include the following: How should countries coordinate on data and privacy issues associated with AI development? Laws and customs regarding data gathering, accumulation, utilization and privacy differ between the United States and Germany. How should issues of data localization and incorporation of local data be harmonized?6 Roadmapping a U.S.-German Agenda for Artificial Intelligence Policy 5 Will AI inevitably lead to an erosion of personal data privacy, or can we improve the framework for granting/revoking permission for data sharing and monitoring how personal data is being used? Our current framework for privacy of personal data relies on the theory of informed consent, and generally does not allow more than binary control (i.e. share vs. don’t share). However, informed consent is often unrealistic (privacy and licensing agreements often cover many pages and are not actually read or understood) and having more fine-grained control over when, how, and for what purposes personal data is shared could be valuable. ENVIRONMENT AI-based industries have the potential to eliminate an enormous amount of waste, delivering products we need, when we need them and reducing inefficiencies within transportation, manufacturing, and logistics. At the same time, improvements in one realm (e.g. speed, efficiency, cost, etc.) may lead to challenges in another (e.g. employment, emissions, etc.). Questions surrounding AI and the environment include the following: Can AI be used to optimize the efficiency and performance of networks of machines? One example might be scheduling the timing of when electricity is drawn from the grid to minimize emissions, or managing fleets of vehicles to reduce traffic. More broadly, how should AI be deployed in the context of the internet of things/connected machines?7 What are the areas in which AI may result in conflicting social goods/costs? In certain areas, AI is likely to enable technologies and business models that have profound social benefits, while simultaneously exerting social costs. One example is self-driving cars. On the one hand, self-driving cars may result in wide-scale carpooling (environmentally beneficial) and dramatically reduce the number of road deaths (socially and economically beneficial). On the other, they may result in the elimination of hundreds of thousands of jobs (socially destructive) or a dramatic expansion in vehicle miles traveled (VMT). AI-based industries have the potential to eliminate an enormous amount of waste. 6 NEW AMERICA ETHICS AI has the potential to improve our utilization of data in ways that eliminate social biases. But sometimes objectively true data can also lead to poor social outcomes. This may result from mislabeling, categorization, or poor sampling. The German Federal Ministry of Transport and Digital Infrastructure has established an Ethics Commission in order to deal with ethical aspects of AI. Some of the questions associated with the ethics of data and AI include: How to deal with racial, gender, and other forms of discrimination promoted by AI-based systems? Should businesses using AI be required to divulge the legal parameters/requirements of their decision basis? Increasingly, AI may be used to make decisions that affect a person’s finances, livelihood, housing, medical care, and other important aspects of their lives. Should there be a method to challenge or review highly consequential decisions taken by AI systems? Given the complex nature of AI processing/decision-making, is this technically feasible? Does AI pose a threat to democracy? There are numerous ways in which AI systems may lead to discriminatory outcomes. For instance, ratings systems for customers/users of a specific service might socially disadvantage one group (e.g. women, men, the elderly, minorities, etc.). In some cases, this may be the result of incentives that align with a particular set of data but don’t adequately address certain social needs. It is easy to foresee a situation in which certain Western biases are prioritized over cultural values from other societies. It is just as easy to imagine a situation in which a non-Western country (e.g. Russia, China, etc.) generates AI that does not adhere to values that many Westerners hold to be universal (e.g. democracy).8 Democratic elections appear to be more vulnerable to information warfare than was previously understood. Would AI exacerbate this situation? Could AI systems be used to manipulate information flow or directly alter voting counts to affect political outcomes? Could AI forge information, such as images, documents, or sound recordings, to cause political damage? How to improve transparency in AI-animated fields? Part of the power of machine learning is the ability to deploy data and complex algorithms to achieve technological advances that would be challenging Roadmapping a U.S.-German Agenda for Artificial Intelligence Policy 7 or impossible for human programmers to achieve. Unfortunately, the complexity of these algorithms often makes them inscrutable. How do we create systems that increase the transparency of these powerful, complex computational tools? Can there be a science of “AI forensics”? In some cases it might be necessary to trace the decisions of an AI to humans. If this is impossible, who should be held responsible for AI decisions that cause harm but cannot be directly traced to human developers or operators? DISTRIBUTION (LABOR, WAGES, DISTRIBUTION, TAXATION, WELFARE, ETC.) AI is bound to improve the productivity of many sectors of the economy and reduce human error in areas such as medicine and finance. At the same time, there are serious questions regarding the labor market implications of these technologies and how to reimagine the social contract in a world where an increasing proportion of economic productivity is accounted for by machines and computers. Some critical questions include the following: Can the economic premise of compensation for human production and labor survive in a world where AI increasingly performs productive and even creative tasks, with less and less human input? income, since we believe more productive people should get more wealth. Who is being “productive” when a job function is automated and no longer needs human input?9 Could a Guaranteed Basic Income (GBI) be implemented within the U.S. and/or German political and legal systems? The idea of a GBI has been introduced as a response to the increasing automation and job displacement portended by AI. What social impact would a GBI have on dimensions such as happiness and life satisfaction, health, and economic growth? What are the limits of reskilling? Our legal and economic systems are fundamentally based on a linkage between productivity and 8 Are there areas in which reskilling efforts should be NEW AMERICA focused? Are there certain characteristics that we can isolate that make a worker more/less likely to be successfully reskilled? Is there a vulnerability index for automation? Can age, education, or other factors predict the vulnerability of certain labor demographics to automation? to risk potential failure because of an extensive familial safety net. Is expanding the social safety net through tools like guaranteed basic income a means of increasing entrepreneurship and economic growth? What are the prospects for reskilling labor displaced by AI? How can we learn from existing efforts within technical schools and successful reskilling programs? How do we improve skills-needs forecasting? The time lag between demand for labor in emerging fields and the training of workers capable of fulfilling those jobs is a persistent and disruptive source of friction within the labor market and society. How do we nurture an entrepreneurial mindset as opposed to identity occupation? For many people, their identity is densely intertwined with occupation. Many families have been working in a specific industry (e.g. assembly line, mining coal, or serving in the military) for generations. Is it possible to create a less fixed, more entrepreneurial mindset that will help citizens adapt? What is the relationship between safety nets and entrepreneurship? Germany’s vocational training programs have demonstrated enormous success at preparing workers for employment in technical fields, and thus served as an example for U.S. policymakers and institutions. However, successfully reskilling workers displaced by automation and technological change remains a challenge for Germany and the United States. What are the areas of employment that are least susceptible to being replaced by AI? Are these the same across borders? Or do they differ because of culture or regulation? From a purely technical perspective, it should be possible to make broad assumptions about which jobs are susceptible to being replaced by algorithms and robots. However, there may be cultural or regulatory factors that impact the likelihood of jobs being replaced by AI in the near future (to 2035). Most of Silicon Valley’s most successful entrepreneurs enjoyed the luxury of being able There are serious questions regarding the labor market implications of these technologies and how to reimagine the social contract in a world where an increasing proportion of economic productivity is accounted for by machines and computers. Roadmapping a U.S.-German Agenda for Artificial Intelligence Policy 9 Notes Stouffer, Keith et al. Guide to Industrial Control Systems (ICS) Security: Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control Systems (DCS), and other control system configurations such as Programmable Logic Controllers (PLC) (National Institute for Standards and Controls 2015) 1 2 Autonomous Weapons: an Open Letter from AI & Robotics Researchers (The Future of Life Institute 2015) See, for example, Schumers Urging Drone Geo-fencing Proposal Passes the Senate New Technology Could Prohibit Drones from Flying Near Airports Sensitive Areas Sporting Events and Above 500ft (Office of Charles E. Schumer 2016) 3 Preparing for the Future of Artificial Intelligence Recommendations 20, 21 (US National Science Technology Council 2016); Federal Automated Vehicles Policy (US Department of Transportation 2016): https:// obamawhitehouse.archives.gov/blog/2016/10/12/ administrations-report-future-artificial-intelligence, https://one.nhtsa.gov/nhtsa/av/av-policy.html. 4 See, for example, Scherer, Matthew. Regulating Artificial Intelligence Systems: Risks, Challenges, Competencies, and Strategies. (Harvard Journal of Law and Technology, Spring 2016): https://papers.ssrn.com/sol3/papers2. cfm?abstract_id=2609777 5 See, for example, Artificial Intelligence, Robotics, Privacy and Data Protection (38th International Conference of Data Protection and Privacy Commissioners 2016) 6 See, for example, DeepMind AI Reduces Google Data Center Cooling Bill by 40% (DeepMind 2016). 7 See, for example, Big Data: A Tool for Inclusion or Exclusion? (Federal Trade Commission 2016). 8 See, for example, Artificial Intelligence and Life in 2030: One Hundred Year Study on Artificial Intelligence (Stanford University 2016). 9 10 NEW AMERICA This report carries a Creative Commons Attribution 4.0 International license, which permits re-use of New America content when proper attribution is provided. This means you are free to share and adapt New America’s work, or include our content in derivative works, under the following conditions: • Attribution. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. For the full legal code of this Creative Commons license, please visit creativecommons.org. If you have any questions about citing or reusing New America content, please visit www.newamerica.org. 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