Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

We commonly think of society as made of and by humans, but with the proliferation of
machine learning and AI technologies, this is clearly no longer the case. Billions of …

Human-centricity is the core value behind the evolution of manufacturing towards Industry
5.0. Nevertheless, there is a lack of architecture that considers safety, trustworthiness, and …

Black box AI systems for automated decision making, often based on machine learning over
(big) data, map a user's features into a class or a score without exposing the reasons why …

Abstract Today, Artificial Intelligence is achieving prodigious real-time performance, thanks
to growing computational data and power capacities. However, there is little knowledge …

This paper discusses educating stakeholders of algorithmic systems (systems that apply
Artificial Intelligence/Machine learning algorithms) in the areas of algorithmic fairness …

AI auditing is a rapidly growing field of research and practice. This review article, which
doubles as an editorial to Digital Society's topical collection on 'Auditing of AI', provides an …

We propose CX-ToM, short for counterfactual explanations with theory-of-mind, a new
explainable AI (XAI) framework for explaining decisions made by a deep convolutional …

The present level of skepticism expressed by courts, legal practitioners, and the general
public over Artificial Intelligence (AI) based digital evidence extraction techniques has been …

The need for AI systems to provide explanations for their behaviour is now widely
recognised as key to their adoption. In this article, we examine the problem of trustworthy AI …