A survey of feature attribution techniques in explainable AI: taxonomy, analysis and comparison
Muhammad Nazir, Edmund De Leon Evangelista, Syed Muhammad Salman Bukhari, Ravi Sharma · Annals of Mathematics and Computer Science · 2025
The feature attribution methods have become central to explainable artificial intelligence (XAI), providing critical insights into how machine learning (ML) models make individual and aggregate decisions. This survey presents a complete taxonomy of feature attribution techniques, organizing them into model-agnostic and model-specific categories while highlighting extensions such as rule-based and attention-based explanations. We analyze formal definitions of each method, mathematical formulations, application contexts, strengths, and limitations. A comparative analysis highlights key trade-offs among model flexibility, computational cost, explanation fidelity, and interpretability. In addition to theoretical perspectives, we provide practical comparisons of selected methods on benchmark tasks to guide real-world applicability. The emerging trends toward global interpretability, hybrid attribution approaches, and human-centered evaluation frameworks are discussed. This survey synthesizes current advancements and presents future directions for developing scalable, robust, and user-aligned feature attribution methods to advance responsible and transparent AI.