Exploring the black box: analysing explainable AI challenges and best practices through stack exchange discussions

Abstract Explainable Artificial Intelligence (XAI) is a crucial domain within research and industry, aiming to develop AI models that provide human-understandable explanations for their decisions. While the challenges in AI, deep learning, and big data have been extensively explored, the specific concerns of XAI developers have received limited attention. To address this gap, we analysed discussions on Stack Exchange websites to delve into these issues. Through a combination of automated and Manual analysis, we identified 6 overarching categories, 10 distinct topics, and 40 sub-topics commonly discussed by developers. Our examination revealed a steady rise in discussions on XAI since late 2015, initially focusing on conceptualisation and practical applications, with a notable surge in activity across all topic categories since 2019. Notably, Concepts and Applications, Tools Troubleshooting, and Neural Networks Interpretation emerged as the most popular topics. Troubleshooting challenges were commonly encountered with tools like SHAP, ELI5, and AIF360, while visualisation issues were prevalent with Yellowbrick and SHAP. Furthermore, our analysis suggests that addressing questions related to XAI poses greater difficulty compared to other machine-learning questions.