Explainability is crucial for complex systems like pervasive smart environments, as they
collect and analyze data from various sensors, follow multiple rules, and control different …

To increase trust in systems, engineers strive to create explanations that are as accurate as
possible. However, if the system's accuracy is compromised, providing explanations for its …

Cyber-physical systems (CPS) are increasingly complex and harder for human users to
understand. Integrating explainability methods within their design is a key challenge for their …

The aim of algorithmic recourse (AR) is generally understood to be the provision of"
actionable" recommendations to individuals affected by algorithmic decision-making …

Both personalisation and explainability have become popular research topics in social
robotics, each capable of improving human-robot interactions. However, challenges have …