
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

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

Position paper on AI for the operation of critical energy and mobility network infrastructures
This position paper outlines AI4REALNET’s approach to applying AI in network infrastructure operations, translating application needs into algorithmic proposals for effective human-AI collaboration in decision-making processes.

Report on meta-analysis on externalities of acceptability and trustworthiness of ADR
This Adra-e deliverable presents an analysis of the externalities surrounding acceptability and trustworthiness in ADR-supported innovative technologies.

An overview of key trustworthiness attributes and KPIs for trusted ML-based systems engineering
When deployed, machine-learning (ML) adoption depends on its ability to actually deliver the expected service safely, and to meet user expectations in terms of quality and continuity of service.