Research and analysis

We review the landscape of AI standards and certification and emphasise their role in the context of trustworthy AI. Third-party certification is a key service in building trust in AI and ML systems and supporting their operationalisation. We argue that…

This document constitutes the Strategic Research Agenda (SRA) for the European Metrology Network for Mathematics and Statistics in Metrology (EMN Mathmet). The EMN Mathmet is an alliance of European National Metrology Institutes (NMIs), Designated Institutes (DIs) and an EMN Partner…

NIST has released a plan for prioritizing federal agency engagement in the development of standards for artificial intelligence (AI). The plan recommends the federal government “commit to deeper, consistent, long-term engagement in AI standards development activities to help the United…

The artificial intelligence (AI) revolution is upon us, with the promise of advances such as driverless cars, smart buildings, automated health diagnostics and improved security monitoring. Many current efforts are aimed to measure system trustworthiness, through measurements of Accuracy, Reliability,…

As individuals and communities interact in and with an environment that is increasingly virtual they are often vulnerable to the commodification of their digital exhaust. Concepts and behavior that are ambiguous in nature are captured in this environment, quantified, and…

In this paper, we make the case that interpretability and explainability are distinct requirements for machine learning systems. To make this case, we provide an overview of the literature in experimental psychology pertaining to interpretation (especially of numerical stimuli) and…

We introduce four principles for explainable artificial intelligence (AI) that comprise fundamental properties for explainable AI systems. We propose that explainable AI systems deliver accompanying evidence or reasons for outcomes and processes; provide explanations that are understandable to individual users;…