
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

Uncertainty-Based Learning of a Lightweight Model for Multimodal Emotion Recognition
In this paper, the authors propose a lightweight neural network architecture that extracts and performs the analysis of multimodal information using the same audio and visual networks across multiple temporal segments.

Towards functional safety management for AI-based critical systems
The webinar provides attendees with a comprehensive understanding of the challenges and opportunities associated with integrating AI into safety-critical systems.

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.