Explanations have gained an increasing level of interest in the AI and Machine Learning
(ML) communities in order to improve model transparency and allow users to form a mental …

Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness
remains elusive and constitutes a key issue that impedes large-scale adoption. Besides …

Some tasks in content processing, eg, natural language processing (NLP), like hate or
offensive speech and emotional or funny text detection, are subjective by nature. Each …

Today's most advanced machine-learning models are hardly scrutable. The key challenge
for explainability methods is to help assisting researchers in opening up these black boxes …

Humans use abstract concepts for understanding instead of hard features. Recent
interpretability research has focused on human-centered concept explanations of neural …

Artificial neural networks offer great classification performances, but their internal model
works as a black box. This can prevent their outcomes to be employed in real-world decision …

Mental fatigue refers to the decline in cognitive abilities that can occur as a result of
prolonged mental exertion. Neuroscientists have been studying mental fatigue for a while …

The focus of recent research has shifted from merely increasing the Deep Neural Networks
(DNNs) performance in various tasks to DNNs, which are more interpretable to humans. The …

Concept bottleneck models (CBMs) include a bottleneck of human-interpretable concepts
providing explainability and intervention during inference by correcting the predicted …

The prediction of ecological toxicity plays an increasingly important role in modern society.
However, the existing models often suffer from poor performance and limited predictive …