
Z-Inspection
Z-Inspection® is a methodology for assessing the trustworthiness of AI systems.



Labelling initiatives, codes of conduct and other self-regulatory mechanisms for artificial intelligence applications
AI has emerged as a critical area of interest to numerous stakeholders across the world.



IMPACT STORIES - from the TAILOR Network of Excellence Centres in AI
The TAILOR Impact Stories is a powerful demonstration of the collective achievements and spirit of the TAILOR network.

STRATEGIC RESEARCH & INNOVATION ROADMAP OF TRUSTWORTHY AI
This document is the first version of the Strategic Research and Innovation Roadmap of the TAILOR project, focussed on Trustworthy Artificial Intelligence through Learning, Optimization and Reasoning.


Handbook of Trustworthy AI
The main goal of the Handbook of Trustworthy AI (HTAI) is to provide to non-experts, researchers and students, an overview of the problem related to the developing of ethical and trustworthy AI systems.



Oratione
Quae cum dixisset, finem ille. Quamquam non negatis nos intellegere quid sit voluptas, sed quid ille dicat. Progredientibus autem aetatibus sensim tardeve potius quasi nosmet ipsos cognoscimus. Gloriosa ostentatio in constituendo summo bono.

Explainable AI for systems with functional safety requirements


Application of the ALTAI tool to power grids, railway network and air traffic management



Towards functional safety management for AI-based critical systems


Position paper on AI for the operation of critical energy and mobility network infrastructures
