The most important issue in the field of explainable AI (XAI) is the challenge of achieving transparency while maintaining model performance. Many advanced AI models, especially deep learning algorithms, operate as “black boxes,” making it difficult for users to understand how decisions are made. The field can move toward more trustworthy and understandable AI systems.

The field of Explainable AI (XAI) has attracted significant attention in recent years. One of the main challenges in XAI is the trade-off between explainability and performance, especially when dealing with complex, high-performing models such as deep learning or ensemble models. The following areas could be interesting for further exploration:

• Human-Centered Explainability
• Explainable Reinforcement Learning
• Ethical and Regulatory concerns such as Fairness and Accountability
• Scalability of Explainability such as distributed and parallel explainability