Currently, explainability represents a major barrier that Artificial Intelligence (AI) is facing in
regard to its practical implementation in various application domains. To combat the lack of …

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 …

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

Understanding black box models has become paramount as systems based on opaque
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …

Abstract Explainable Artificial Intelligence (xAI) is an established field with a vibrant
community that has developed a variety of very successful approaches to explain and …

The field of explainable artificial intelligence (XAI) aims to uncover the inner workings of
complex deep neural models. While being crucial for safety-critical domains, XAI inherently …

With a variety of local feature attribution methods being proposed in recent years, follow-up
work suggested several evaluation strategies. To assess the attribution quality across …

Ensuring both transparency and safety is critical when deploying Deep Neural Networks
(DNNs) in high-risk applications such as medicine. The field of explainable AI (XAI) has …

Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging
and trusting statistical and deep learning models, as well as interpreting their predictions …

Deep Neural Networks (DNNs) are known to be strong predictors, but their prediction
strategies can rarely be understood. With recent advances in Explainable Artificial …