

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

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

Online course on Data Spaces for Mobility
Discover the world of mobility data spaces in this online course tailored for mobility professionals with a solid background in mobility planning, operations, data, and management.

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

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.