Michael I. Jordan is the Pehong Chen Distinguished Professor in the departments of electrical engineering and computer science and of statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive, biological and social sciences. Jordan 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.
How AI Fails Us, and How Economics Can Help –…
How AI Fails Us, and How Economics Can Help – Michael I. Jordan
Artificial intelligence (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. This perspective is familiar to economics researchers, and a first goal in this work is to bring economics into contact with computer science and statistics. The long-term goal is to provide a broader conceptual foundation for emerging real-world AI systems, and to upend received wisdom in the computational, economic and inferential disciplines.
