A Synthetic Ground-Truth Framework for the Evaluation of Explainable AI Methods

arXiv:2609.30397 · cs.AI · Submitted 2026-09-24 · Read on arXiv

cs.AI

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/miquelmn/fb2ts

Terminology

• Benchmark — 10× in this paper · explained in 2 episodes
• Fidelity — 9× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Explainability — 4× in this paper · explained in 3 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Benchmarking — 3× in this paper · explained in 3 episodes
• Calibration — 3× in this paper · explained in 7 episodes
• Feature Selection — 3× in this paper · explained in 4 episodes
• Model-Agnostic — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Activation Function — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Mask Generation — 2× in this paper · explained in 2 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• AUC — 1× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Accountability — 1× in this paper · explained in 2 episodes
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Adversarial Robustness — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Synthetic Artificial Intelligence Ground Truth — 1× in this paper
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Data Representing Ground-Truth Explanations to Evaluate XAI Methods
• Focus! Rating XAI Methods and Finding Biases
• Towards Ground Truth Explainability on Tabular Data
• Captum: A unified and generic model interpretability library for PyTorch
• Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms
• Synthetic Benchmarks for Scientific Research in Explainable Machine Learning
• Exploring SAIG Methods for an Objective Evaluation of XAI
• "Why Should I Trust You?": Explaining the Predictions of Any Classifier
• Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
• Benchmarking Attribution Methods with Relative Feature Importance

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
• VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection