The XAI Frontier: What Comes After SHAP and LIME

Key Points

• This study aims to identify emerging trends and frameworks in explainable artificial intelligence (XAI) following SHAP and LIME.
• Systematic review of existing literature on XAI frameworks
• Analysis of algorithmic transparency and model interpretability approaches
• Identification of new methodologies and tools for improving explainability
• New frameworks for explainability were proposed that enhance user understanding of AI decisions.
• Findings suggest that combining existing methods with novel approaches can lead to greater algorithm transparency.
• Research highlights the need for standardization in evaluating XAI methods.