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 …

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 …

The proliferation of Large Language Models (LLMs) has s fueled a shift in robot learning
from automation towards general embodied Artificial Intelligence (AI). Adopting foundation …

Ensuring both transparency and safety is critical when deploying Deep Neural Networks
(DNNs) in high-risk applications such as medicine. The field of explainable AI (XAI) has …

Abstract Deep Neural Networks (DNNs) demonstrated remarkable capabilities in learning
complex hierarchical data representations, but the nature of these representations remains …

The continuous development of artificial intelligence (AI) theory has propelled this field to
unprecedented heights, owing to the relentless efforts of scholars and researchers. In the …

Abstract Concept Bottleneck Models (CBMs) have recently been proposed to address the
'black-box'problem of deep neural networks, by first mapping images to a human …

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 …

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 …

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 …