The Lab intends to encourage debate and reflection on the responsible use of Artificial Intelligence, acting as a meeting place for the international community, such as the Z-Inspection® assessment method for Trustworthy AI and its affiliated labs.
New survey: E-Lomake - Survey of Large Language Models (LLMs) in Higher-Education Information and Data Management Plan here! |
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The labs intend to encourage debate and reflection on the responsible use of Artificial Intelligence, acting as a meeting place for the international "community" of researchers, politicians, sector leaders and citizens, creating events, offering the possibility of teaching and doing research.
The Lab aims at connecting with other labs, researching and participating in use-cases with other labs in the world, applying for funding (EU/national), integrating trustworthy AI in teaching, and inviting teachers from other labs to lecture for students at the university college.
The Z-Inspection® approach is a validated assessment method that helps organizations deliver ethically sustainable, evidence-based, trustworthy, and user-friendly AI-driven solutions. The method is published in IEEE Transactions on Technology and Society.
Z-Inspection is listed in the new OECD Catalogue of AI Tools & Metrics.
Z-Inspection® (website) is distributed under the terms and conditions of the Creative Commons (Attribution-NonCommercial-ShareAlike CC BY-NC-SA) license.
Lab members:
Dr. Frode Ramstad Johansen (head, innovation processes)
Dr. Gunnar Anderson (organisational development)
Dr. Pedro Kringen (medical, advisor, Z-Inspection® initiative)
Prof. Hadi Strømmen Lile (law and ethics)
Dr. Fredrik Andersen (philosophy, health)
Dr. Leonora Onarheim Bergsjø (education, digital ethics)
Prof. Roberto Zicari (advisor, Z-Inspection® initiative)
Publications involving members:
Kringen, P., et al. Using LLMs without trust: the adoption–trust paradox as institutional governance failure in academic ecosystems. AI & Soc (2026).
Kringen, P. et al. (2026). Trustworthy Federated Machine Learning in Healthcare: A Sociotechnical and Risk-Based Evaluation Using the Z-Inspection® Method. In: Särestöniemi, M., Singh, D., Jarva, E., Reponen, J. (eds) Digital Health and Wireless Solutions: Integrating AI, LLMs and Multimodal Health Data for Next-Generation Decision Support. NCDHWS 2026. Communications in Computer and Information Science, vol 3010. Springer, Cham.
Vetter, D., et. al. (2023). Lessons Learned from Assessing Trustworthy AI in Practice. Digital Society, 2(3), 35.
Zicari, R. V., et. al. (2022). How to Assess Trustworthy AI in Practice. arXiv:2206.09887 [cs.CY]
Zicari, R. V., et. al. (2021). On assessing trustworthy AI in healthcare. Machine learning as a supportive tool to recognize cardiac arrest in emergency calls. Frontiers in Human Dynamics, 3, 673104.
Zicari, R. V., et. al. (2021). Co-design of a Trustworthy AI System in Healthcare: Deep Learning based Skin Lesion Classifier. Frontiers in Human Dynamics, 3, 688152.
Zicari, R. V., et. al. (2021). Z-Inspection®: a process to assess trustworthy AI. IEEE Transactions on Technology and Society, 2(2), 83-97.
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New paper: Using LLMs without trust: the adoption–trust paradox as institutional governance failure in academic ecosystems.
Pedro Kringen, Alessio Gallucci, Elisabeth Hildt, Frode Ramstad Johansen, Magnus Westerlund & Christa Tigerstedt.
Quoting our report:
"We call this the adoption-trust paradox... The data are consistent with a self-reinforcing cycle: educators report clear benefits and assume widespread student use, while trusting the outputs little, and continued use does not appear to build that trust."
Link: https://doi.org/10.1007/s00146-026-03323-z
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