THEMATIC SESSION #06
MEASURE-AI: MEtrology for AI in indUstrial and cybeR-physical systEms
ORGANIZED BY
Amna Dridi
Amna Dridi
Birmingham City University
Iain Rice
Iain Rice
Birmingham City University
Muhammad Afzal
Muhammad Afzal
Birmingham City University
THEMATIC SESSION DESCRIPTION
The rapid adoption of Artificial Intelligence (AI) across industrial and cyber-physical systems (CPS) -including manufacturing systems, digital twins, smart infrastructure, robotics, and immersive industrial environments - has outpaced the development of rigorous, reproducible measurement and evaluation frameworks. While conventional AI benchmarks focus primarily on static task performance, real-world CPS demand metrology-informed approaches capable of capturing dynamic behaviour, uncertainty, robustness, safety, and human-in-the-loop interactions under operational conditions.
This workshop addresses the growing gap between AI system capabilities and measurable, verifiable performance in deployed CPS. It focuses on measurement science, validation methodologies, and evaluation protocols for AI-enabled systems operating in real-time, interactive, and safety-critical environments. Advanced application domains - such as industrial extended reality (XR), digital twins, and emerging brain–computer interface (BCI) systems - are considered as representative CPS testbeds, enabling the exploration of transferable metrology principles applicable across multiple industrial sectors.
MEASURE-AI brings together researchers, practitioners, and industry stakeholders to discuss how AI performance can be systematically measured, calibrated, monitored, and audited throughout the system lifecycle, supporting the development of trustworthy, explainable, and deployable AI in cyber-physical environments.
TOPICS
Topics of interest include, but are not limited to:
- Metrology frameworks for AI-enabled industrial and cyber-physical systems;
- Measurement and validation of AI performance in real-time and operational environments;
- Uncertainty, robustness, and reliability assessment of AI-driven CPS;
- Data quality, provenance, and traceability in industrial AI pipelines;
- Human-in-the-loop and human-AI collaboration measurement;
- Evaluation of AI in digital twins, industrial XR, and simulation-to-reality workflows;
- Multi-modal AI assessment (vision, language, sensor data, biosignals);
- Reproducibility, benchmarking, and standardisation for AI in CPS;
- Case studies from manufacturing, smart infrastructure, healthcare, and rehabilitation;
- Emerging challenges for metrology in adaptive and autonomous systems.
ABOUT THE ORGANIZERS
Dr. Amna Dridi is a Lecturer in Data Science at Birmingham City University with over ten years of experience in applied Artificial Intelligence (AI) and Natural Language Processing (NLP). She completed her PhD in AI and NLP in the UK and has prior research experience in Italy. She has served as Guest Editor for international journal special issues and as a programme committee member and peer reviewer for numerous international conferences and workshops.
Prof. Iain Rice is a Professor of Industrial AI with a strong track record in leading data-driven AI research and education in the UK. His work covers Generative AI and AI systems in healthcare, defence, and industry, with expertise in AI governance, ethics, GDPR, and regulatory compliance.
Dr. Muhammad Afzal is an Associate Professor in Artificial Intelligence. His expertise spans artificial intelligence, data science, big data analytics, and statistics, with extensive teaching and research experience across international institutions. He has published widely in leading journals and conferences and serves as Associate Editor, Guest Editor, and reviewer for multiple peer-reviewed journals, with active involvement in international research projects and conference committees.
