Input-gradient products provide a first-order approximation of the model’s sensitivity.
Outline Introduction: Defining the bridge between model architecture and output behavior using gradients. Key Concepts: Understanding the Taylor expansion, the…
April 2026
High-dimensional feature spaces necessitate dimensionality reduction before applyingSHAP.
High-Dimensional Feature Spaces: Why Dimensionality Reduction is a Prerequisite for SHAP Introduction In the era of Big Data, we are…
Axiomatic properties such as Efficiency and Symmetry guide the formal evaluation ofXAI methods.
Contents 1. Introduction: The black-box dilemma and why intuitive explanations aren’t enough.2. Key Concepts: Defining Axiomatic properties (Efficiency, Symmetry, Dummy,…
Computational complexity scales linearly with the number of features for most XAImethods.
The Efficiency of Explainability: Why XAI Complexity Scales Linearly with Features Introduction As machine learning models evolve from simple linear…
Perturbation-based methods like LIME require multiple model evaluations per instance explained.
The Computational Tax: Why Perturbation-Based Explainability (LIME) Demands High Resources Introduction As machine learning models evolve from simple linear regressions…
Model-specific methods generally offer lower computational latency than perturbation-based approaches.
Contents 1. Introduction: Defining the trade-off between speed and transparency in AI interpretability.2. Key Concepts: Differentiating between Model-Specific (Gradient-based) and…
The choice of explanation method often depends on the required interpretability/granularity.
The Choice of Explanation Method: Aligning Interpretability with Granularity Introduction In the modern era of artificial intelligence, the “black box”…
Neural networks often exhibit jagged gradient landscapes, complicating saliency map interpretation.
The Jagged Frontier: Why Neural Network Gradient Landscapes Complicate Saliency Maps Introduction Artificial Intelligence has moved beyond the “black box”…
Model-specific methods require access to the architecture’s internal connectivity.
Outline Introduction: The divergence between model-agnostic and model-specific explainability. Key Concepts: Understanding “White-Box” methods, gradients, and internal weights. Step-by-Step Guide:…
Robustness refers to the stability of explanations under minor perturbations of input data.
Outline Introduction: The “Trust Gap” in AI and why unstable explanations create liability. Key Concepts: Defining Robustness, Sensitivity analysis, and…