As autonomous intelligence systems demonstrate remarkable capabilities in extracting contextual information from images and sensor data, a paradox emerges: the more sophisticated these systems become, the less humans trust their decisions. This keynote explores the critical tension between AI capability and the "black box nervousness" that hinders real-world adoption.
Modern context-aware systems excel at processing multi-modal data from smart cities, autonomous vehicles, and IoT networks. However, their inability to explain why decisions are made creates deployment barriers and undermines confidence. This opacity is particularly problematic where systems must interpret complex relationships between environmental conditions, temporal patterns, and visual information.
Explainable AI (XAI) offers a solution by transforming black box models into transparent systems. Through attention mechanisms, saliency maps, and feature importance visualization, XAI enables stakeholders to understand not just what an autonomous system decided, but why and how.
Drawing from sustainable AI and smart cities research, this talk presents practical applications of AI to solve problems. The presentation argues that true autonomous intelligence must integrate explainability from the design phase. For the next generation of data scientists, demanding transparency in AI systems is both a technical skill and an ethical imperative.
Dr. Sian Lun Lau received his Dr.-Ing. and MSc in Electrical Communication Engineering from the University of Kassel, Germany. He also holds a BEng with Hons in Electronics and Telecommunications Engineering from Universiti Malaysia Sarawak (UNIMAS).
During his nine years (2004 – 2013) as a researcher at the Chair for Communication Technology (ComTec) at the University of Kassel, he has worked and managed various German National- and EU-funded research projects. Among them are EU IST-MobiLife, ITEA S4ALL, BMBF MATRIX and EU-SEAM4US.
He joined Sunway University, Malaysia, in February 2013 as a senior lecturer and Head of the Department of Computing and Information Systems until March 2021. He is currently a Professor at the Department of Smart Computing and Cyber Resilience at the Faculty of Engineering and Technology.
He is currently a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and serves as the Vice Chair of the IEEE Computer Society Malaysia Chapter for the term 2023/2024 and 2025/2026. His research interests include ubiquitous computing, sustainable smart city, context-awareness, and applied machine learning. His recent research projects include ISPF BEST, EDUFI CyberBridge, ImpactXChange and SustHack, MOSTI TED2 RADIC and US DOD DeepSpray+.