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 …
X Li, F Zhang, L Zheng, J Guo - Journal of Hazardous Materials, 2024 - Elsevier
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 …