Displaying 5 resources
Video/Webinars Video/Webinars

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.

Category
Certification and Compliance, Trustworthiness
Target audience
ADR Experts and Associations, Private Sector, Researchers and Academic
Source
Adra-e
White paper White paper

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

Category
Action and Interaction Technologies, Real-time context aware decision making, Policy, Regulation, Certificates, and Standards, Best practices in deployment, Legal framework and regulation, Recommendations towards policy changes, Reasoning and decision-making Technologies, Hybrid Decision Making, Trustworthiness, Systems, methodologies, hardware, and tools, Support tools, System architectures
Target audience
ADR Experts and Associations, Individual Citizens/Members of the Society, Policy Makers, Private Sector, Public Sector, Researchers and Academic
Source
Adra-e
Video/Webinars Video/Webinars

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.

Category
Real-time context aware decision making, Certification and Compliance, Standards, Trustworthiness
Target audience
Private Sector, Researchers and Academic
Source
Adra-e
Software resources Software resources

Decentralized-gnn

A package for implementing and simulating decentralized Graph Neural Network algorithms for classification of peer-to-peer nodes.

Category
System architectures, Technology methodologies and landscape
Target audience
ADR Experts and Associations, Researchers and Academic
Source
Adra-e
Software resources Software resources

PandA: Unsupervised learning of parts and appearances in the feature maps of GANs

We propose an architecture-agnostic approach that jointly discovers factors representing spatial parts and their appearances in an entirely unsupervised fashion.

Category
System architectures
Target audience
ADR Experts and Associations, Researchers and Academic
Source
Adra-e