Thanks to the exponential growth in computing power and vast amounts of data, artificial
intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be …

This paper provides a brief analytical review of the current state‐of‐the‐art in relation to the
explainability of artificial intelligence in the context of recent advances in machine learning …

Feature attributions based on the Shapley value are popular for explaining machine
learning models. However, their estimation is complex from both theoretical and …

Abstract Explainable Artificial Intelligence (XAI) is an emerging research topic of machine
learning aimed at unboxing how AI systems' black-box choices are made. This research field …

Deep learning methods have been very effective for a variety of medical diagnostic tasks
and have even outperformed human experts on some of those. However, the black-box …

Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …

Artificial intelligence (AI) and machine learning (ML) have come a long way from the earlier
days of conceptual theories, to being an integral part of today's technological society. Rapid …

Artificial intelligence (AI) models are increasingly finding applications in the field of
medicine. Concerns have been raised about the explainability of the decisions that are …

Autonomous navigation in unknown environment is still a hard problem for small Unmanned
Aerial Vehicles (UAVs). Recently, some neural network-based methods are proposed to …

Path attribution methods are a gradient-based way of explaining deep models. These
methods require choosing a hyperparameter known as the baseline input. What does this …