While the evaluation of explanations is an important step towards trustworthy models, it
needs to be done carefully, and the employed metrics need to be well-understood …

Abstract The Model Parameter Randomisation Test (MPRT) is highly recognised in the
eXplainable Artificial Intelligence (XAI) community due to its fundamental evaluative …

In the language domain, as in other domains, neural explainability takes an ever more
important role, with feature attribution methods on the forefront. Many such methods require …

The increasingly widespread adoption of large language models has highlighted the need
for improving their explainability. We present context length probing, a novel explanation …

The Model Parameter Randomisation Test (MPRT) is widely acknowledged in the
eXplainable Artificial Intelligence (XAI) community for its well-motivated evaluative principle …

Background Discrimination between patients affected by inflammatory bowel diseases and
healthy controls on the basis of endoscopic imaging is an challenging problem for machine …

Feature attribution methods (FAs), such as gradients and attention, are widely employed
approaches to derive the importance of all input features to the model predictions. Existing …

Deep Learning of neural networks has gained prominence in multiple life-critical
applications like medical diagnoses and autonomous vehicle accident investigations …

Feature attribution methods (FAs), such as gradients and attention, are widely employed
approaches to derive the importance of all input features to the model predictions. Existing …

Explainable AI (XAI) is a rapidly evolving field that aims to improve transparency and
trustworthiness of AI systems to humans. One of the unsolved challenges in XAI is estimating …