Deep Neural Networks (DNNs) excel at learning complex abstractions within their internal
representations. However, the concepts they learn remain opaque, a problem that becomes …

Artificial Intelligence (AI) plays an increasingly integral role in decision-making processes. In
order to foster trust in AI predictions, many approaches towards explainable AI (XAI) have …

Nowadays, artificial intelligence in medicine plays a leading role. This necessitates the need
to ensure that artificial intelligence systems are not only high-performing but also …

In the field of Computer Vision (CV), the degree to which two objects, eg two classes, share
a common conceptual meaning, known as semantic similarity, is closely linked to the visual …

In deep learning-based image classification, the entropy of a neural network's output is often
taken as a measure of its uncertainty. We introduce an explainability method that identifies …

Artificial intelligence (AI) has transformative potential in veterinary pathology in tasks ranging
from cell enumeration and cancer detection to prognosis forecasting, virtual staining …

Explainable AI methods facilitate the understanding of model behaviour, yet, small,
imperceptible perturbations to inputs can vastly distort explanations. As these explanations …

In light of deep neural network applications emerging in diverse fields–eg, industry,
healthcare or finance–weaknesses and failures of these models might bare unacceptable …

Machine learning models are increasingly being applied in safety-critical domains.
Therefore, ensuring their trustworthiness and reliability has become a priority. Uncertainty …

Explainability and uncertainty quantification are two pillars of trustable artificial intelligence.
However, the reasoning behind uncertainty estimates is generally left unexplained …