Trustworthiness is a critical issue in artificial intelligence (AI), especially for real-world applications. It is impossible to deploy AI in the real world without its being trustworthy. However, the connotation and extension of AI trustworthiness are not entirely clear. There has not been a single definition that is accepted by all researchers. This talk starts from a brief recall of trustworthy systems in the literature and tries to understand any potential differences between classical trustworthy systems and modern-day trustworthy AI. It argues that AI ethics is a crucial part of trustworthy AI, which was not featured in classical trustworthy systems. The talk then presents a short summary of AI ethics and governance. It presents potential synergies among responsible AI (RAI), ESG and SDGs. It points out the challenges of AI governance because of the high complexity and multi-dimensional nature of ethical principles, e.g., transparency. Potential technical solutions will be mentioned, although not in any details. The talk ends with some concluding remarks.
Professor Xin Yao, Vice-President (Research and Innovations) and the Tong Tin Sun Chair Professor of Machine Learning at Lingnan University, Hong Kong, China. He is an IEEE Fellow and a Fellow of the Hong Kong Academy of Engineering. He served as the President (2014-15) of IEEE Computational Intelligence Society (CIS) and the Editor-in-Chief (2003-08) of IEEE Transactions on Evolutionary Computation. His major research interests include AI ethics, evolutionary computation, neural network ensembles, and multi-objective machine learning. His research won the 2001 IEEE Donald G. Fink Prize Paper Award; 2010, 2016 and 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Awards; 2011 IEEE Transactions on Neural Networks Outstanding Paper Award; 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist); and many other best paper awards at conferences. He has received several prestigious awards, including the 2012 Royal Society Wolfson Research Merit Award, the 2013 IEEE CIS Evolutionary Computation Pioneer Award, and the 2020 IEEE Frank Rosenblatt Award.
Clean water and clean air are vital for public health. This project focuses on developing high-efficiency and environmentally sustainable filters for removing harmful air/water pollutants. The team has developed novel architectures and functionalities for the filters to achieve high permeance, high removal efficiency, and excellent reusability.
