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 …

The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace,
making it difficult for novel researchers and practitioners to have a bird's-eye view picture of …

Unintended biases in machine learning (ML) models have the potential to introduce undue
discrimination and exacerbate social inequalities. The research community has proposed …

While the increased use of AI in the manufacturing sector has been widely noted, there is
little understanding on the risks that it may raise in a manufacturing organisation. Although …

The promised merits of data-driven innovation in general and algorithmic systems in
particular hardly need enumeration. However, as decision-making tasks are increasingly …

“Extended reality”(XR) systems work to blend the physical and digital worlds. This means
that XR is highly contextual: its functionality, operation and therefore consequences are …

Explainable AI (XAI) is often promoted with the idea of helping users understand how
machine learning models function and produce predictions. Still, most of these benefits are …

Investigative journalists and public defenders conduct the essential work of examining,
reporting, and arguing critical cases around police use-of-force and misconduct. In an ideal …

Following the success of deep learning (DL) in research, we are now witnessing the fast and
widespread adoption of artificial intelligence (AI) in daily life, influencing the way we act …

Recent AI-related scandals have shed a spotlight on accountability in AI, with increasing
public interest and concern. This paper draws on literature from public policy and …