August 27, 2021
Phebe Vayanos先生講演会 「Towards Robust, Interpretable, and Fair Social and Public Health Interventions」Lecture by Dr. Phebe Vayanos: "Towards Robust, Interpretable, and Fair Social and Public Health Interventions"
日時：2021年8月27日（金曜日） 4:00-5:30pm (日本時間）
Assistant Professor, Industrial and Systems Engineering and Computer Science, University of Southern California
Associate Director, CAIS Center for Artificial Intelligence in Society.
Towards Robust, Interpretable, and Fair Social and Public Health Interventions
In the last decades, significant advances have been made in AI, ML, and optimization. Recently, systems relying on these technologies are being transitioned to the field with the potential of having tremendous influences on people and society. With increase in the scale and diversity of deployment of algorithm-driven decisions in the open world come several challenges including the need for robustness, interpretability, and fairness which are confounded by issues of data scarcity and bias, tractability, ethical considerations, and problems of shared responsibility between humans and algorithms.
In this talk, we focus on the problems of homelessness and public health in low resource and vulnerable communities and present research advances in AI, ML, and optimization to address one key cross-cutting question: how to allocate scarce intervention resources in these domains while accounting for the challenges of open world deployment? We will show concrete improvements over the state of the art in these domains based on real world data. We are convinced that, by pushing this line of research, AI, ML, and optimization can play a crucial role to help fight injustice and solve complex problems facing our society.
Dr. Phebe Vayanos is an Assistant Professor of Industrial & Systems Engineering and Computer Science at the University of Southern California, and Associate Director of the CAIS Center for Artificial Intelligence in Society.
Her research is focused on optimization, artificial intelligence, and machine learning, in particular on algorithmic fairness, interpretability, and robustness. Her mission is to design systems that drive efficient decision-making while addressing bias and inequality, to tackle problems such as homelessness, substance use, suicide prevention, and organ allocation.
She holds a PhD degree in Operations Research and an MEng degree in Electrical & Electronic Engineering, both from Imperial College London. She is a recipient of the NSF CAREER award and the INFORMS Diversity, Equity, and Inclusion Ambassador Program Award.
主催：東京大学 Beyond AI研究推進機構 B’AI Global Forum