Interpretable machine learning aims at unveiling the reasons behind predictions returned by
uninterpretable classifiers. One of the most valuable types of explanation consists of …

Machine learning is increasingly used to inform decision making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …

Machine learning is increasingly used to inform decision-making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …

Counterfactual explanations are viewed as an effective way to explain machine learning
predictions. This interest is reflected by a relatively young literature with already dozens of …

The increasing deployment of machine learning as well as legal regulations such as EU's
GDPR cause a need for user-friendly explanations of decisions proposed by machine …

The past decade has shown a surge in the use and application of machine learning and
deep learning models across various domains. One such domain is credit scoring, where …

With the increasing deployment of machine learning systems in practice, transparency and
explainability have become serious issues. Contrastive explanations are considered to be …

Satellite image classification is crucial in various applications such as urban planning,
environmental monitoring, and land use analysis. In this study, the authors present a …

Learning Vector Quantization (LVQ) is one of the most widely used classification
approaches. LVQ faces a problem as when the size of data grows large it becomes slower …

The Kolmogorov-Smirnov (KS) test is popularly used in many applications, such as anomaly
detection, astronomy, database security and AI systems. One challenge remained …