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Panel Member Bios

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Panel Member Bios

Michael L. Littman (Study Panel chair)

Michael holds a Ph.D. in computer science. He is the Royce Family Professor of Teaching Excellence in Computer Science at Brown University (US), where he studies reinforcement learning and its interaction with people. A past chair of the Association for the Advancement of Artificial Intelligence (AAAI) conference and the International Conference on Machine Learning (ICML), he is a fellow of the ACM and AAAI, and is a 2020-2021 AAAS Leshner Fellow on public engagement in AI. He is currently a member of AI Hub and was communications co-chair at the Neural Information Processing Systems conference.

Ifeoma Ajunwa

Ifeoma holds a Ph.D. in sociology, as well as a J.D. She is an associate professor in the University of North Carolina School of Law (US) and founding director of their artificial intelligence and decision-making research program. Her research interests are at the intersection of law and technology, with a particular focus on the ethical governance of workplace technologies, as well as on diversity and inclusion in the labor market and the workplace. She is a 2019 recipient of the NSF CAREER Award and a 2018 recipient of the Derrick A. Bell Award.

Guy Berger

Guy holds a Ph.D. in economics and a degree in math. He is principal economist at LinkedIn (US). His research includes analysis of the data at LinkedIn, such as identifying the top countries where people are moving to for work, the industries most likely to hire career-switchers, and the most in-demand skills. He warns that the need for soft skills will skyrocket in the future of robots and automation. He works with companies and governments to help people find jobs.

Craig Boutilier

Craig holds a Ph.D. in computer science. He is principal scientist at Google (US). He previously worked in academia for 24 years. He works on various aspects of decision-making under uncertainty, social choice and mechanism design with a current focus on sequential decision models and their application to recommender systems. He is a Fellow of the Royal Society of Canada (RSC), the Association for Computing Machinery (ACM) and the Association for the Advancement of Artificial Intelligence (AAAI). He received the 2018 ACM/SIGAI Autonomous Agents Research Award and served as Editor-in-Chief of the Journal of Artificial Intelligence Research.

Morgan Currie

Morgan is a lecturer in data and society in science, technology and innovation studies at the University of Edinburgh (UK). Her research looks at open and administrative data, automation in the welfare state, activists’ data practices, social justice and the city, web maps and cultural mapping. She was awarded a Ph.D. in information studies at the University of California, Los Angeles, and a master’s degree in new media at the University of Amsterdam, before holding a postdoc in the Digital Civil Society Lab at Stanford University. Morgan was one of the organizers of the AI100 Coding Caring workshop.

Finale Doshi-Velez

Finale holds a Ph.D. in computer science. She is a John L. Loeb associate professor of computer science at the Harvard Paulson School of Engineering and Applied Sciences (US). Her research spans probabilistic models, reinforcement learning, and interpretable machine learning, largely focused toward healthcare applications. The undergraduate machine-learning course she teaches includes both technical material and significant exposure to machine learning with a sociotechnical system perspective.

Gillian Hadfield

Gillian holds a Ph.D. in economics and a J.D. She is a professor of both law and strategic management at the University of Toronto (Canada), where she holds the Schwartz Reisman Chair in Technology and Society and is inaugural director of the Schwartz Reisman Institute for Technology and Society. She served on the Steering Committee for the AAAI AI Ethics and Society Conference and was a co-organizer of the first NeurIPS Workshop on Cooperative AI. Her research is focused on what she calls the science of normativity—modeling societal norms—as well as the design of legal and regulatory systems, and contract design and law.

Michael C. Horowitz

Michael holds a Ph.D. in government and a degree in political science. He is professor of political science and the interim director of Perry World House at the University of Pennsylvania (US) and is co-author of the book Why Leaders Fight. Michael previously worked for the Office of the Undersecretary of Defense for Policy in the Department of Defense. His research interests include technology and global politics, military innovation, the role of leaders in international politics, and forecasting.

Charles Isbell

Charles holds a Ph.D. in computer science. His research focuses on applying statistical machine learning to building autonomous agents that must live and interact with large numbers of other intelligent agents, some of whom may be human. Charles received the Black Engineer of the Year Modern Day Technology Leader Award (2009). He is the John P. Imlay, Jr. Dean of the College of Computing at Georgia Tech (US) and is a fellow of Association for the Advancement of Artificial Intelligence (AAAI) and the Association for Computing Machinery (ACM). He has presented congressional testimony on his work in both online education and machine learning.

Hiroaki Kitano

Hiroaki holds a Ph.D. in computer science. He is president and CEO of Sony Computer Science Laboratories and Sony AI, as well as a professor at Okinawa Institute of Science and Technology Graduate University (Japan). He carried out research on AI and natural language processing at Carnegie Mellon University and on systems biology at the California Institute of Technology. He is a recipient of the prestigious Computers and Thought Award (1993) honoring early career scientists in AI. Hiroaki is developing AI systems for making scientific discoveries and is closely involved in policy-setting in Japan.

Karen Levy

Karen holds a Ph.D. in sociology and a J.D. She is an assistant professor of information science at Cornell University (US) and an associated faculty member at Cornell Law School. Her research considers the legal, organizational, social, and ethical aspects of data-intensive technologies, focused on inequality, care/intimacy, privacy, and labor. Karen is a New America National Fellow and she previously served as a law clerk in the United States Federal Courts. Karen was one of the organizers of the AI100 Prediction in Practice workshop.

Terah Lyons

Terah holds a degree in social studies with a focus on network theory and complex systems, and her professional work has focused primarily on technology policy. She was the founding executive director of the Partnership on AI and is a former policy advisor to the US chief technology officer in the White House Office of Science and Technology Policy (OSTP), where she helped establish and direct the White House Future of Artificial Intelligence Initiative. Terah currently sits on the steering committee of the AI Index.

Melanie Mitchell

Melanie holds a Ph.D. in computer science. She is the Davis Professor of Complexity at the Santa Fe Institute and professor of computer science (currently on leave) at Portland State University (US). Her current research focuses on conceptual abstraction, analogy-making, and visual recognition in AI systems. Melanie is the author or editor of six books on artificial intelligence, cognitive science, and complex systems, the latest being Artificial Intelligence: A Guide for Thinking Humans.

Julie Shah

Julie holds a Ph.D. in autonomous systems. She is an associate professor in the department of aeronautics and astronautics at MIT (US) and heads MIT’s Interactive Robotics Group. Julie is also an associate dean of social and ethical responsibilities of computing. Her work aims to imagine the future of work by designing collaborative robot teammates that enhance human capability. She has translated her work to manufacturing assembly lines, healthcare applications, transportation and defense. She was also a member of the 2016 AI100 Study Panel and previously worked at Boeing Research and Technology.

Steven Sloman

Steve holds a Ph.D. in psychology. He is a professor in the cognitive, linguistic, and psychological sciences department at Brown University (US), specializing in higher-level cognition, especially causal reasoning and collective knowledge. He has studied how the systems that constitute thought interact to produce conclusions, conflict, and conversation, and how our interpretation of how the world works influences how we evaluate events and decide what to do. He co-authored a book on collective cognition, The Knowledge Illusion: Why We Never Think Alone.

Shannon Vallor

Shannon holds a Ph.D. in philosophy. She is the Baillie Gifford Chair in the ethics of data and AI at the University of Edinburgh (UK). Her research focuses on the ethics of AI, robotics, data science, digital media and other emerging technologies. She was a visiting researcher and AI ethicist at Google. She served on the steering committee of ACM/AAAI's AI Ethics and Society conference. Shannon also chairs the Scottish government's Data Delivery Group and the University of Edinburgh's AI and Data Ethics Advisory Board.

Toby Walsh

Toby holds a Ph.D. in artificial intelligence. He is Scientia Professor of Artificial Intelligence at the University of New South Wales (Australia) and served as scientific director of the information technology research center NICTA (now Data61). His research addresses aspects of AI in automated reasoning, constraint programming, social choice, and game theory. He was editor-in-chief of the Journal of Artificial Intelligence Research and helped release an open letter calling for a ban on offensive autonomous weapons. He is the author of two trade books on artificial intelligence.