Artificial Intelligence (AI) systems involve diverse components, such as data, models, users
and predicted outcomes. To elucidate these different aspects of AI systems, multifaceted …

Although AI is spread across all domains and many authors stated that providing
explanations is crucial, another question comes into play: How accurate are those …

Recent advancements in AI have coincided with ever-increasing efforts in the research
community to investigate, classify and evaluate various methods aimed at making AI models …

Although AI is spread across all domains and many authors stated that providing
explanations is crucial, another question comes into play: How accurate are those …

The rapid adoption of Artificial Intelligence (AI) has brought automation and problem-solving
capabilities in various fields, including healthcare. However, a significant challenge lies in …

The rapid evolution of Artificial Intelligence (AI) algorithms in decision-making has
profoundly impacted various fields, offering promising advancements. As these advances …

The lack of transparency of powerful Machine Learning systems paired with their growth in
popularity over the last decade led to the emergence of the eXplainable Artificial Intelligence …

Introduction Many Explainable AI (XAI) systems provide explanations that are just clues or
hints about the computational models-Such things as feature lists, decision trees, or saliency …

Despite the fact that machine learning has been applied in innumerable domains, its models
have usually operated in a black box fashion, ie without revealing the rationale behind their …

Explainability is increasingly considered a critical component of artificial intelligence (AI)
systems, especially in high-stake domains where AI systems' decisions can significantly …