Explainable Artificial Intelligence: A Review and Case Study on Model-Agnostic Methods

Abstract

Explainable Artificial Intelligence (XAI) has emerged as an essential aspect of artificial intelligence (AI), aiming to impart transparency and interpretability to AI black-box models. With the recent rapid expansion of AI applications across diverse sectors, the need to explain and understand their outcomes becomes crucial, especially in critical domains. In this paper, we provide a comprehensive review of XAI techniques, emphasizing their methodologies, strengths, and potential limitations. Furthermore, we present a case study employing six model-agnostic XAI techniques, offering a comparative analysis of their effectiveness in explaining a black-box model related to a healthcare scenario. Our experiments not only showcase the applicability and distinctiveness of each technique but also provide insights to researchers and practitioners seeking sui table XAI methodologies for their projects. We conclude with a discussion on future research perspectives in the field of explainable AI.