Chair: Yulei Wu, University of Exeter, UK
Vice-Chair: Noura Limam, University of Waterloo, Canada
Vice-Chair: Tarek Salhi, Vodafone, UK
Vice-Chair: Yan Zhang, University of Oslo, Norway
Advisor: Dusit Niyato, Nanyang Technological University, Singapore
Advisor: Dimitra Simeonidou, University of Bristol, UK
Advisor: Filip De Turck, Ghent University, Belgium
Advisor: Chonggang Wang, InterDigital, USA
Advisor: Albert Zomaya, The University of Sydney, Australia
Networks and digital infrastructure, such as telecom, transport, energy and water, have been underpinning the functioning of a society and economy. With the fast development of Internet of Things (IoT), Industrial IoT (IIoT), sensors, edge/cloud computing, and artificial intelligence/machine learning (AI/ML) technologies, many traditional critical infrastructure systems, which were isolated somehow, are now exposed, and connected to the Internet, transforming to digital infrastructure. Such a transformation heavily depends on efficient data collection, effective data analysis, and intelligent decision making, but can gain lots of benefits including creating intelligent services such as smart grid and offering a range of advantages for cost savings and efficiencies on network operation and security management. As the evolution and transformation move on, future networks and digital infrastructure would turn into increasingly complex systems.
Many AI/ML models have been developed to improve the efficiency of operating and managing such network and digital infrastructure systems, enhance the performance of smart services they support, as well as build up the security, robustness, resilience and reliability of the infrastructure. These models have not been widely adopted by network and digital infrastructure operators due to their potential ethical issues, such as fairness, transparency and accountability. These ethical issues make AI/ML models untrusted on managing network and digital infrastructure. Operators don’t want to be surprised with any unexpected actions given by models under certain infrastructure states. Insurance companies are also reluctant to trust ‘black-box’ models without sensible explainaibility. In addition, AI/ML models are introduced to automate various tasks for managing networks and digital infrastructure. If we target 100% automation, we must let AI/ML models themselves make decisions autonomously and they may encounter various ethical issues similar with the trolly problem. AI/ML models must be designed with moral theory and ethical principles considered at the outset.
To have a wider acceptance and implementation of AI/ML models in managing future networks and digital infrastructure, we need systematically search and study the related ethical issues and develop ethical AI/ML solutions. This requires cross-disciplinary research at the intersection of computing, engineering, and social science. This SIG will focus on the technical challenges of developing ethically compliant AI/ML solutions for future networks and digital infrastructure.
The areas of interests include, but are not limited to, the following:
Stuart Allen, Cardiff University, UK
Raouf Boutaba, University of Waterloo, Canada
Prosper Chemouil, Cnam (Conservatoire national des arts et métiers), France
Trung Q. Duong, Queen’s University Belfast, UK
Carlos Raniery P. dos Santos, Federal University of Santa Maria (UFSM), Brazil
Xiaoming Fu, Gottingen University, Germany
Carol Fung, Concordia University, USA
Alex Galis, University College London, UK
Lajos Hanzo, University of Southampton, UK
David Leslie, The Alan Turing Institute, UK
Ruidong Li, Kanazawa University, Japan
Niccolò Tempini, University of Exeter, UK
Fulvio Valenza, Politecnico di Torino, Italy
Kun Yang, University of Essex, UK
Jin Zhao, Fudan University, China
Zuqing Zhu, University of Science and Technology of China, China
Should you be interested in joining this IEEE SIG, please get in touch with Dr. Yulei Wu.