How AI Fails Us and How Economics Can Help

This series on artificial intelligence explores recent breakthroughs of AI, its broader societal implications and its future potential. In this presentation, Michael Jordan, professor of Electrical Engineering and Computer Science and Statistics at UC Berkeley, discusses the how to connect research in economics with computer science and statistics, with a long-term goal of providing a broader conceptual foundation for emerging real-world AI systems, and to upend received wisdom in the computational, economic and inferential disciplines. Jordan argues that AI has focused on a paradigm in which intelligence inheres in a single agent, and in which agents should be autonomous so they can exhibit intelligence independent of human intelligence. Thus, when AI systems are deployed in social contexts, the overall design is often naive. Such a paradigm need not be dominant. In a broader framing, agents are active and cooperative, and they wish to obtain value from participation in learning-based systems. Agents may supply data and resources to the system, only if it is in their interest. Critically, intelligence inheres as much in the system as it does in individual agents. Jordan's research interests bridge the computational, statistical, cognitive, biological and social sciences. He is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences, and a foreign member of the Royal Society. He was a plenary lecturer at the International Congress of Mathematicians in 2018. He received the Ulf Grenander Prize from the American Mathematical Society in 2021, the IEEE John von Neumann Medal in 2020, the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize from the Cognitive Science Society in 2015 and the ACM/AAAI Allen Newell Award in 2009. Series: "Data Science Channel" [Science] [Business] [Show ID: 38858]