Deep neural networks (DNNs) have found their way into many applications with potential
impact on the safety, security, and fairness of human-machine-systems. Such require basic …

Explainable Artificial Intelligence (XAI) has emerged as a crucial research area to address
the interpretability challenges posed by complex machine learning models. In this survey …

Deep Neural Networks are prone to learning spurious correlations embedded in the training
data, leading to potentially biased predictions. This poses risks when deploying these …

Research on Explainable Artificial Intelligence has recently started exploring the idea of
producing explanations that, rather than being expressed in terms of low-level features, are …

Abstract Analysis of how semantic concepts are represented within Convolutional Neural
Networks (CNNs) is a widely used approach in Explainable Artificial Intelligence (XAI) for …

Multimodal Emotion Recognition refers to the classification of input video sequences into
emotion labels based on multiple input modalities (usually video, audio and text). In recent …

Concept-based XAI (C-XAI) approaches to explaining neural vision models are a promising
field of research, since explanations that refer to concepts (ie, semantically meaningful parts …

Current approaches for explaining deep learning systems applied to musical data provide
results in a low-level feature space, eg, by highlighting potentially relevant time-frequency …

An important line of research attempts to explain convolutional neural network (CNN) image
classifier predictions and intermediate layer representations in terms of human …

Continual Test-Time Adaptation (CTTA) is an emerging and challenging task where a model
trained in a source domain must adapt to continuously changing conditions during testing …