Explainable Artificial Intelligence for Business and Economics: Methods, Applications and Challenges
Qi Lyu, Shaomin Wu · Expert Systems · 2025
ABSTRACT In recent years, artificial intelligence (AI) has made significant strides in research and shown great potential in various application fields, including business and economics (B&E). However, AI models are often black boxes, making them difficult to understand and explain. This challenge can be addressed using eXplainable Artificial Intelligence (XAI), which helps humans understand the factors driving AI decisions, thereby increasing transparency and confidence in the results. This paper aims to provide a comprehensive understanding of the state‐of‐the‐art research on XAI in B&E by conducting an extensive literature review. It introduces a novel approach to categorising XAI techniques from three different perspectives: samples, features and modelling method. Additionally, the paper identifies key challenges and corresponding opportunities in the field. We hope that this work will promote the adoption of AI in B&E, inspire interdisciplinary collaboration, foster innovation and growth and ensure transparency and explainability.