Explainable Artificial Intelligence: Advancements and Limitations

Explainable artificial intelligence (XAI) has emerged as a crucial field for understanding and interpreting the decisions of complex machine learning models, particularly deep neural networks. This review presents a structured overview of XAI methodologies, encompassing a diverse range of techniques designed to provide explainability at different levels of abstraction. We cover pixel-level explanation strategies such as saliency maps, perturbation-based methods and gradient-based visualisations, as well as concept-based approaches that align model behaviour with human-understandable semantics. Additionally, we touch upon the relevance of XAI in the context of weakly supervised semantic segmentation. By synthesising recent developments, this paper aims to clarify the landscape of XAI methods and offer insights into their comparative utility and role in fostering trustworthy AI systems.