VIP / Speakers
Making AI Trustworthy
Time / Place:
⏱️ 10/7 (Thur.) 19:20-19:45 at Online Track 1
Abstract:
In recent years, exciting technological breakthroughs in AI research and technology have been witnessed across different domains, ranging from computer vision, natural language processing, robotics, medical diagnosis, game playing, to scientific discovery, among others. While we are expecting an AI-driven revolution to transform our industry and bring something good to our daily lives, it is also necessary to inspect its potential negative influence and possible failure modes that could incur undesirable ethical, societal, social, technological, and environmental impacts.
In this talk, I will share the journey toward making AI trustworthy as an industrial practitioner and as an AI researcher, including the following four main topics: fairness, explainability, robustness, and transparency.
For each topic, I will provide motivation, case studies, and technical tools for infusing trust into AI technology and regulation.
Finally, I will share my vision on how practicing trustworthy AI can create a new ecosystem that ensures responsible, reliable, and safe use of our AI technology and how trustworthy AI can create new business and research opportunities.
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Biography:
- 陳品諭 Pin-Yu Chen Website: https://www.pinyuchen.com
- IBM Research AI; MIT-IBM Watson AI Lab; RPI-IBM AIRC IBM Thomas J. Watson Research Center, NY, USA / Research Staff Member
Dr. Pin-Yu Chen is a research staff member at IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA. He is also the chief scientist of RPI-IBM AI Research Collaboration and PI of ongoing MIT-IBM Watson AI Lab projects. Dr. Chen received his Ph.D. degree in electrical engineering and computer science from the University of Michigan, Ann Arbor, USA, in 2016. Dr. Chen’s recent research focuses on adversarial machine learning and robustness of neural networks.
His long-term research vision is building trustworthy machine learning systems. At IBM Research, he received the honor of IBM Master Inventor and several research accomplishment awards. His research works contribute to IBM open-source libraries including Adversarial Robustness Toolbox (ART 360) and AI Explainability 360 (AIX 360).
He has published more than 40 papers related to trustworthy machine learning at major AI and machine learning conferences, given tutorials at IJCAI’21, CVPR(’20,’21), ECCV’20, ICASSP’20, KDD’19, and Big Data’18, and organized several workshops for adversarial machine learning.
He received a NeurIPS 2017 Best Reviewer Award, and was also the recipient of the IEEE GLOBECOM 2010 GOLD Best Paper Award.
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