
Webinar "Towards Transparent, Safe and Trustworthy AI for critical infrastructures"
This webinar focuses on the development of safe, explainable, and algorithmically transparent methods as part of the AI4REALNET project.


Fairlearn
Fairlearn is an open-source, community-driven project to help data scientists improve fairness of AI systems.

Explainable AI for systems with functional safety requirements
Explainable AI (XAI) is vital for making AI decision-making processes transparent and understandable to human experts, and for ensuring safety and regulatory compliance.

Webinar: Knowledge-Assisted AI Applications for Real-World Network Infrastructure
This webinar showcases how the AI4REALNET project is driving innovation in critical infrastructure through advanced AI applications.
Structured Power Grid Simulation Dataset for Machine Learning: Failure and Survival Events in Grid2Op's L2RPN WCCI 2022 Environment
This dataset was developed for and used in the paper titled "Fault Detection for Agents in Power Grid Topology Optimization: A Comprehensive Analysis" by Malte Lehna, Mohamed Hassouna, Dmitry Degtyar, Sven Tomforde, and Christoph Scholz,

Webinar "Industry-driven Use Cases"
AI4REALNET project covers the perspective of AI-based solutions addressing critical systems (electricity, railway, and air traffic control), modelled by networks that can be simulated and traditionally operated by humans and where AI complements a