[HTML][HTML] Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions
Understanding black box models has become paramount as systems based on opaque
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …
How interpretable machine learning can benefit process understanding in the geosciences
Abstract Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering
new opportunities to improve our understanding of the complex Earth system. IML goes …
new opportunities to improve our understanding of the complex Earth system. IML goes …
Disentangled explanations of neural network predictions by finding relevant subspaces
Explainable AI aims to overcome the black-box nature of complex ML models like neural
networks by generating explanations for their predictions. Explanations often take the form of …
networks by generating explanations for their predictions. Explanations often take the form of …
[HTML][HTML] Explainable ai for time series via virtual inspection layers
The field of eXplainable Artificial Intelligence (XAI) has witnessed significant advancements
in recent years. However, the majority of progress has been concentrated in the domains of …
in recent years. However, the majority of progress has been concentrated in the domains of …
Concept-based explainable artificial intelligence: A survey
The field of explainable artificial intelligence emerged in response to the growing need for
more transparent and reliable models. However, using raw features to provide explanations …
more transparent and reliable models. However, using raw features to provide explanations …