主题：On AI Bias and Fairness
时间：2020年6月8日（周一）北京时间上午9:00-10:15
地点：Zoom线上会议（会议 ID：685 6605 8304）
报告人：丁文璿 人工智能与商业分析教授，法国里昂商学院全球商业智能中心副主任
摘要：
Artificial intelligence (AI) is becoming deeply embedded in people’s everyday life, and machines are making more and more algorithmic decisions for humans. However, the “intelligence” function in AI relies heavily on learning functions that learn from training data. That is, through discovering patterns in the data, AI systems establish their intelligence. As a result, they often work impressively well when applied to the exact environments on which they are trained. But, if the environment differs, sometimes even in small ways from the environment on which they are trained, mistakes can become endemic with no obvious route for the victims of the errors to seek redress. In this talk, we review theoretical background of AI and examine where decision bias and discrimination come from. We then present a novel theory-based individual dynamic learning model to overcome discrimination and emphasize on a causal inference to enable explanation.
主讲人简介：
Dr. Ding obtained Ph.D. in Cognitive Science and Information Technology from Carnegie Mellon University, USA. She conducts forefront theoretical and empirical research on human-level artificial intelligence (AI), machine learning, and their applications in business analytics, digital transformation and marketing, and mobile health. Her research has appeared in various top-tier journals including Information Systems Research, Journal of the Academy of Marketing Science, Decision Support Systems, Springer Computational Intelligence Series, the Proceedings of Association for Advancement of Artificial Intelligence, Defense & Security Analysis, Journal of Defense Modeling and Simulation, Oxford Journal of Management Mathematics, and Safety Science. She is a member of the IEEE standards committee on Wellbeing Metrics Standard for Ethical AI and Autonomous Systems. She co-organized 2019 AAAI (Association for Advancement of Artificial Intelligence) Spring Symposium on Interpretable AI at Stanford University, USA.