Artificial intelligence (AI) holds significant promise for advancing natural disaster
management through the use of predictive models that analyze extensive datasets, identify …

Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging
and trusting statistical and deep learning models, as well as interpreting their predictions …

The past decade has seen significant progress in artificial intelligence (AI), which has
resulted in algorithms being adopted for resolving a variety of problems. However, this …

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 …

Deep learning models like chatGPT exemplify AI success but necessitate a deeper
understanding of trust in critical sectors. Trust can be achieved using counterfactual …

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 …

Artificial Intelligence (AI) has become an important component of many software
applications. It has reached a point where it can provide complex and critical decisions in …

Generative AI (GenAI) represents a shift from AI's ability to “recognize” to its ability to
“generate” solutions for a wide range of tasks. As generated solutions and applications grow …

This study presents a new approach to understand the causes of groundwater drought
events with interpretable deep learning (DL) models. As prerequisites, accurate long short …

Artificial intelligence (AI) is having a growing impact in many areas related to drug discovery.
However, it is still critical for their adoption by the medicinal chemistry community to achieve …