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管理學(xué)workshop:The Bounded Intelligence of AI: Superficiality and Deceivability

發(fā)布日期：2024-05-24 12:00    來(lái)源：

報告題目：The Bounded Intelligence of AI: Superficiality and Deceivability

時(shí)間: 5月24日，上午10點(diǎn)

地點(diǎn)：承澤園344教室

報告人: Mengyue Su

報告摘要：

While AI is to augment or replace human beings in many domains of decision making and executions, accurate and timely replicating or capturing the know-hows of human decisions and actions becomes imperative, as AI has to be, at least as intelligent as, or even more intelligent than, those human experts and masters whose data are being fed to AI during its training and learning iterations. Just as human beings are subject to bounded rationality due to their limited capacities in information processing, AI, too, is subject to bounded intelligence, in the sense that it could appear not as smart as expected, due to its limited capacities in replicating and capturing human expertise. Such bounded intelligence of AI hinges on two underlying factors: Superficiality and Deceivability. Superficiality refers to the inability of AI to fully replicate the human expertise due to those natural or technical barriers during its learning process. Deceivability refers to the inability of AI to accurately capture the human expertise during its learning process. Understanding such boundedness of AI and the specific mechanisms of superficiality and deceivability will help us better appreciate the usefulness and limitations of AI and taking measures to both remedy and utilize the bounded intelligence of AI.

報告人簡(jiǎn)介：

Dr. Mengyue Su is an assistant professor of Management at the HeXie Management Research Centre, Xi’an Jiaotong-Liverpool University. She earned her Ph.D. in Management from the National School of Development, Peking University. Her research interests focus on organizational learning, knowledge management, dynamic capability, and corporate so- cial responsibility. She has published papers in Organizational Dynamics and Academy of Management Best Paper Proceedings and has won a Distinguished Paper Award from the Strategic Management (STR) Division of the Academy of Management in 2023.


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