Explainable Artificial Intelligence: a Systematic Review
This is an incremental review that organizes existing XAI literature for researchers and practitioners.
The paper tackles the problem of uninterpretable machine learning models by systematically reviewing Explainable AI (XAI) methods, clustering them into a hierarchical classification system and summarizing the state-of-the-art.
Explainable Artificial Intelligence (XAI) has experienced a significant growth over the last few years. This is due to the widespread application of machine learning, particularly deep learning, that has led to the development of highly accurate models but lack explainability and interpretability. A plethora of methods to tackle this problem have been proposed, developed and tested. This systematic review contributes to the body of knowledge by clustering these methods with a hierarchical classification system with four main clusters: review articles, theories and notions, methods and their evaluation. It also summarises the state-of-the-art in XAI and recommends future research directions.