Keynote Speaker - 2025

Abstract 

 Trustworthiness is a critical issue in artificial intelligence (AI), especially for real-world applications. It is impossible to apply AI in the real world without its being trustworthy. However, the connotation and extension of trustworthiness are not entirely clear to the scientific community. There has not been a single definition that is accepted by all researchers. Nevertheless, the vast majority of researchers agree that AI trustworthiness should include at least accuracy, reliability, robustness, safety, security, privacy, fairness, transparency, controllability, maintainability, etc. Firstly, this talk reviews very briefly AI ethics, which is closely related to AI trustworthiness. Secondly, the talk examines the fairness and explainability issues of machine learning models. It is argued that many aspects of trustworthiness, such as fairness and explainability, are inherently multi-dimensional. In other words, there are many dimensions to properties like fairness and explainability. They cannot be defined by any single numerical measures. Multi-objective thinking is needed. This talk advocates multi-objective evolutionary learning as an approach to enhancing AI trustworthiness. Fairness and explainability are used as two examples to demonstrate how multi-objective evolutionary learning can be used to improve fairness and explainability of learned models. Thirdly, the talk discusses more fundamental issues in the current research of AI explainability. Finally, the talk ends with some concluding remarks.