
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.

Holistic framework for AI in critical network infrastructures
This document establishes the main foundations of the AI4REALNET project, in particular, the following key outcomes: - The formal specification of domain-specific use cases (UCs), replicating real-world operating scenarios involving human operator

AI for Teachers
AI for Teachers is a website dedicated to supporting the integration of Artificial Intelligence knowledge throughout K-12 learning.

Don’t ask if AI is good or fair, ask how it shifts power
Opinion piece by Pratyusha Kalluri in Nature

Is ethical AI possible?
An interview with Timnit Gebru, the founder of the Distributed AI Research Institute.

Pygmalion Displacement: When Humanising AI Dehumanises Women
Paper exploring the relationship between women and AI.