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 …

With the extensive application of deep learning (DL) algorithms in recent years, eg, for
detecting Android malware or vulnerable source code, artificial intelligence (AI) and …

Modern deep learning methods have long been considered black boxes due to the lack of
insights into their decision-making process. However, recent advances in explainable …

The inclusion of Explainability of Artificial Intelligence (xAI) has become a mandatory
requirement for designing and implementing reliable, interpretable and ethical AI solutions …

State-of-the-art text classification models are becoming increasingly reliant on deep neural
networks (DNNs). Due to their black-box nature, faithful and robust explanation methods …

Reliable deployment of machine learning models such as neural networks continues to be
challenging due to several limitations. Some of the main shortcomings are the lack of …

LIME has emerged as one of the most commonly referenced tools in explainable AI (XAI)
frameworks that is integrated into critical machine learning applications--eg, healthcare and …

Post-hoc interpretability methods play a critical role in explainable artificial intelligence (XAI),
as they pinpoint portions of data that a trained deep learning model deemed important to …

This paper comprehensively examines Large Language Models (LLMs) in sentiment
analysis, specifically focusing on financial markets and exploring the correlation between …

Rising usage of deep neural networks to perform decision making in critical applications like
medical diagnosis and fi-nancial analysis have raised concerns regarding their reliability …