Counterfactual explanations are increasingly used as an Explainable Artificial Intelligence
(XAI) technique to provide stakeholders of complex machine learning algorithms with …

Imbalanced data poses a significant challenge in classification as model performance is af-
fected by insufficient learning from minority classes. Balancing methods are often used to …

Artificial intelligence and its derivative technologies are not only playing a role in the fields of
medicine, economy, policing, transportation, and natural science computing today but also …

Different explainable AI (XAI) methods are based on different notions of 'ground truth'. In
order to trust explanations of AI systems, the ground truth has to provide fidelity towards the …

Decision processes of computer vision models—especially deep neural networks—are
opaque in nature, meaning that these decisions cannot be understood by humans. Thus …

AI methods referred to as interpretable are often discredited as inaccurate by supporters of
the existence of a trade-off between interpretability and accuracy. In many problem contexts …

The underlying hypothesis of knowledge-based explainable artificial intelligence is the data
required for data-centric artificial intelligence agents (eg, neural networks) are less diverse …

One of the most hotly debated topics in technology is the transparency between AI models
and humans. As artificial intelligence (AI) continues to permeate various sectors, the …

One of the most hotly debated topics in technology is the transparency between AI models
and humans. As artificial intelligence (AI) continues to permeate various sectors, the …

This thesis explores Human-XAI interaction and the vital role of user motivation in providing
feedback to XAI systems in industrial settings, focusing on the design of Incremental …