ADR Awareness Centre

Explainable AI (XAI) is vital for making AI decision-making processes transparent and understandable to human experts, and for ensuring safety and regulatory compliance.

This document presents the responses from industry (operators of critical infrastructures) to the Assessment List for Trustworthy AI (ALTAI) questionnaire for three domains and specific use cases: power grid, railway network, and air traffic manag

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

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

Inspired by scenario writing methods to foster discussion on the societal implications of technology and regulation, the paper develops a ‘legal fiction scenario’ to anticipate the impact of the proposed European 

Holistic AI conducted a Bias Audit for Hired to demonstrate compliance ahead of NYC Local Law 144 (“NYC Bias Audit” legislation), which will come into effect on 1st January 2023.