Gradient based Feature Attribution in Explainable AI: A Technical Review

Gradient-based explanation methods are increasingly important for high-stakes AI applications like healthcare and autonomous driving, where understanding why a model makes a decision is critical for safety and accountability, yet the field still lacks a rigorous, universal definition of what "explainable AI" actually means.

There are multiple ways to evaluate whether an explanation is actually good, including both human evaluation (asking people if explanations make sense) and quantitative evaluation (using mathematical measures), but the paper identifies that measuring explanation quality remains a general challenge across the XAI field.

Gradient-based explanations have evolved significantly over time, with the paper organizing these methods into four distinct categories and tracing their chronological development, suggesting that researchers should understand both current state-of-the-art approaches and their specific limitations before choosing which method to use.

Gradient-based explanation methods face specific technical challenges beyond general XAI problems, indicating that even when using these popular techniques for neural networks, practitioners should be aware that these methods have particular weaknesses that may affect the reliability of their explanations.

The explosion of black-box AI models has created an urgent need for explainability research, but the overwhelming number of different XAI approaches makes it difficult for organizations and practitioners to navigate which explanation methods are most trustworthy and appropriate for their specific applications.