Information Technologies Institute (ITI) of the Centre for Research and Technology Hellas (CERTH)
Dr. Marija Jankovic is a Senior Researcher at the Information Technologies Institute (ITI) of the Centre for Research and Technology Hellas (CERTH). She holds a Ph.D., MSc, and BSc in Information Systems from the University of Belgrade, Faculty of Organizational Sciences, where she also served for a decade as a teaching assistant. Her current research interests focus on technical interoperability, quality assurance, testing languages, and the application of AI in software engineering. Dr. Jankovic has led and contributed to numerous EU-funded projects across domains such as smart manufacturing, secure IoT, and human-centric AI systems. She possesses extensive experience in standardization, having served as a guest and postdoctoral researcher at the U.S. National Institute of Standards and Technology (NIST), where her work focused on supply chain integration and smart manufacturing process design. Additionally, she actively contributes to international standardization bodies, including the Object Management Group (OMG) and the Open Applications Group (OAGi). Since June 2023, she has been serving as Vice-Chair of the ETSI Technical Committee on Methods for Testing and Specification (TC MTS), supporting the evolution of testing methodologies and the standardization of AI systems.
Keynote speaker: dr Marija Janković
link to session
Establishing trust in Artificial Intelligence (AI) systems transcends mere compliance with regulatory frameworks. It requires clear, consistent, and meaningful communication of AI system qualities to diverse stakeholders. The ETSI Technical Committee on Methods for Testing and Specification (TC MTS) addresses this challenge by proposing a harmonized documentation approach specifically designed to fulfill EU AI Act requirements while bridging gaps identified in existing practices.
This keynote presents the TC MTS initiative, highlighting how standardized and harmonized documentation fosters stakeholder trust, reduces risks, and ensures clarity throughout the AI lifecycle. The approach defines explicit documentation strategies that are purpose-driven (why document?), stakeholder-specific (for whom?), context-sensitive (what and when?), and methodologically robust (how?). It integrates diverse documentation formats, such as datasheets, model cards, and interactive knowledge graphs, enhancing comprehensibility, traceability, and regulatory alignment.
Participants will gain insights into best practices, practical tools, and illustrative scenarios developed within ETSI TC MTS. The discussion will emphasize how structured documentation not only ensures regulatory compliance but also strengthens stakeholder confidence, mitigates operational risks, and supports long-term sustainable and ethical AI deployment.