? 演讲主题 The Human Side of AI Training 人工智慧訓練中的人類行為與相互影響
??? 讲者 美國聖路易斯華盛頓大學電腦科學與工程學系 何建儒副教授
? 时间 2026年9月22日（星期二）13:00–15:00
? 地点 管理大樓 11 樓 AI 講堂
? 直播连结 ?
? 注意事项 本演講無需報名，請於演講时间直接進入 Teams 線上會議室或前往實體地点參加。
? 讲者簡介 何建儒博士現任美國聖路易斯華盛頓大學（WashU）電腦科學與工程學系副教授，曾於康乃爾大學擔任博士後研究員，目前兼任長庚大學人工智慧學系客座副教授。其研究領域涵蓋機器學習、人機互動與協作、人類運算以及演算法與行為經濟學之交會，特別關注人類行為與 AI 系統學習決策間的雙向影響。研究成果發表於 PNAS、ICML、NeurIPS、WWW、AAAI、IJCAI、ACL、NAACL、AAMAS 等國際權威期刊與頂級會議，並擔任 ACM Conference on Human-AI Complementarity and Alignment（HCOMP）2026 議程共同主席（Program Co-Chair）。
? 演讲摘要 近年來，人工智慧快速發展，而這些進展在很大程度上仍仰賴人類所提供的資料、回饋、判斷與示範。然而，人類並非只是被動的資料來源：人類的行為會受到 AI 系統與訓練機制的影響，而人類行為本身的特性，也可能進一步影響 AI 學習的結果。
本演講將探討在人工智慧訓練過程中，人類行為及人與 AI 之間的相互影響，如何改變我們蒐集與使用訓練資料的方式。讲者將介紹一系列相關研究，包括：
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人們在得知行為將用於訓練 AI 時如何改變自身決策。
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如何考量人類示範的可學習性與脆弱性以改善 AI 學習成果。
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如何结合人类与大型语言模型，更有效率地取得与估计人类判断。
这些研究共同指出，在训练人工智慧时，我们不仅需要关注资料本身，更需要理解资料背后的人类行為及人机互动所产生的相互影响。
公告单位：智慧运算学院
承办人：洪语彤
联络分机：409-2501
Speaker: Prof. Chien-Ju Ho, WashU Department of Computer Science and Engineering
Date and Time: Tuesday, Septermber 22 , 2026, 13:00–15:00
Venue: 11th Floor, Management Building AI Lecture Hall,
Meeting Link: https://reurl.cc/5XD8Kn or scan the QR code on the poster
About the Speaker:
Chien-Ju Ho is an Associate Professor of Computer Science and Engineering at Washington University in St. Louis (WashU). He is currently on sabbatical and serving as a Visiting Associate Professor at 91猫先生. His research lies at the intersection of machine learning, human-AI interaction and collaboration, human computation, and algorithmic and behavioral economics. In particular, his work examines how human behavior shapes the learning and decision making of AI systems, and how AI systems in turn influence people. His research has appeared in venues including PNAS, ICML, NeurIPS, ACL, NAACL, WWW, AAAI, and AAMAS. He also serves as Program Co-Chair of ACM Conference on Human-AI Complementarity and Alignment (HCOMP) 2026.
Lecture Abstract：
Recent advances in artificial intelligence depend heavily on data, feedback, judgments, and demonstrations provided by humans. Yet humans are not simply passive sources of training data: their behavior can be shaped by AI systems and by the process of training them, while the characteristics of human behavior can in turn affect what AI systems learn. In this talk, I will examine human behavior and mutual influence in AI training, and discuss how taking these factors into account can change the way we collect and use training data. I will present a series of studies examining how people change their decisions when they know their behavior will be used to train AI; how accounting for the learnability and fragility of human behavior can improve learning from human demonstrations; and how humans and large language models can be combined to more efficiently elicit and estimate human judgments. These studies highlight the importance of understanding not only the data used to train AI, but also the human behaviors and interactions through which those data are generated.
Contact person：Tiffany Hung
Contact number：409-2501
