How We Talk About AI (And Why It Matters)
Ryan Calo (Univ. of Washington School of Law)
Ryan Calo is the Lane Powell and D. Wayne Gittinger Associate Professor at the University of Washington School of Law. He is a faculty co-director (with Batya Friedman and Tadayoshi Kohno) of the University of Washington Tech Policy Lab, a unique, interdisciplinary research unit that spans the School of Law, Information School, and Paul G. Allen School of Computer Science and Engineering. Professor Calo holds courtesy appointments at the University of Washington Information School and the Oregon State University School of Mechanical, Industrial, and Manufacturing Engineering.
Guiding and Implementing AI
Susan Athey (Stanford University, Graduate School of Business and Economics):
Susan Athey is The Economics of Technology Professor at Stanford Graduate School of Business. She received her bachelor’s degree from Duke University and her Ph.D. from Stanford, and she holds an honorary doctorate from Duke University. She previously taught at the economics departments at MIT, Stanford and Harvard. In 2007, Professor Athey received the John Bates Clark Medal, awarded by the American Economic Association to “that American economist under the age of forty who is adjudged to have made the most significant contribution to economic thought and knowledge.” She was elected to the National Academy of Science in 2012 and to the American Academy of Arts and Sciences in 2008. Professor Athey’s research focuses on the intersection of machine learning and econometrics, marketplace design, and the economics of digitization. She advises governments and businesses on marketplace design and platform economics, serving as consulting chief economist to Microsoft for a number of years, and serving on the boards of Expedia, Lending Club, Rover, Ripple, and Turo.
Specifying AI Objectives as a Human-AI Collaboration Problem
Anca Dragan (UC Berkeley, EECS)
Anca Dragan is an Assistant Professor in EECS at UC Berkeley, where she runs the InterACT lab. Her goal is to enable robots to work with, around, and in support of people. Anca did her PhD in the Robotics Institute at Carnegie Mellon University on legible motion planning. At Berkeley, she helped found the Berkeley AI Research Lab, is a co-PI for the Center for Human-Compatible AI, and has been honored by the Sloan fellowship, the NSF CAREER award, the Okawa award, MIT’s TR35, and an IJCAI Early Career Spotlight.
The Value of Trustworthy AI
David Danks (Carnegie-Mellon University, Dept. of Philosophy)
Abstract: There are an increasing number of calls for “AI that we can trust,” but rarely with any clarity about what ‘trustworthy’ means or what kind of value it provides. At the same time, trust has become an increasingly important and visible topic of research in AI, HCI, and HRI communities. In this talk, I will first unpack the notion of ’trustworthy’, from both philosophical and psychological perspectives, as it might apply to an AI system. In particular, I will argue that there are different kinds of (relevant, appropriate) trustworthiness, depending on one’s goals and modes of interaction with the AI. There is not just one kind of trustworthy AI, even though trustworthiness (of the appropriate type) is arguably the primary feature that we should want in an AI system. Trustworthiness is both more complex, and also more important, than standardly recognized in the public calls-to-action (and this analysis connects and contrasts in interesting ways with others).
David Danks is the L.L. Thurstone Professor of Philosophy & Psychology, and Head of the Department of Philosophy, at Carnegie Mellon University. He is also an adjunct member of the Heinz College of Information Systems and Public Policy, and the Center for the Neural Basis of Cognition. His research interests are at the intersection of philosophy, cognitive science, and machine learning, using ideas, methods, and frameworks from each to advance our understanding of complex, interdisciplinary problems. In particular, Danks has examined the ethical, psychological, and policy issues around AI and robotics in transportation, healthcare, privacy, and security. He has received a McDonnell Foundation Scholar Award, an Andrew Carnegie Fellowship, and funding from multiple agencies.