As firms are moving towards data-driven decision making, they are facing an emerging
problem, namely, algorithmic bias. Accordingly, algorithmic systems can yield socially …

The application of Artificial Intelligence (AI) across a wide range of domains comes with both
high expectations of its benefits and dire predictions of misuse. While AI systems have …

Artificial intelligence (AI) has elicited much attention across disciplines and industries (Hyder
et al., 2019). AI has been defined as “a system's ability to correctly interpret external data, to …

Abstract Previous research in Explainable Artificial Intelligence (XAI) suggests that a main
aim of explainability approaches is to satisfy specific interests, goals, expectations, needs …

As AI-powered systems increasingly mediate consequential decision-making, their
explainability is critical for end-users to take informed and accountable actions. Explanations …

As AI systems demonstrate increasingly strong predictive performance, their adoption has
grown in numerous domains. However, in high-stakes domains such as criminal justice and …

Academic and policy proposals on algorithmic accountability often seek to understand
algorithmic systems in their socio-technical context, recognising that they are produced by …

A surge of interest in explainable AI (XAI) has led to a vast collection of algorithmic work on
the topic. While many recognize the necessity to incorporate explainability features in AI …

In this article, we develop the concept of Transparency by Design that serves as practical
guidance in helping promote the beneficial functions of transparency while mitigating its …

AI systems are adopted in numerous domains due to their increasingly strong predictive
performance. However, in high-stakes domains such as criminal justice and healthcare, full …