Faithfulness Test

Faithfulness testing in explainable AI (XAI) aims to evaluate how accurately explanations of a model's predictions reflect its actual decision-making process. Current research focuses on developing robust and reliable metrics for assessing faithfulness across various model architectures, including large language models (LLMs) and convolutional neural networks (CNNs), often employing techniques like input perturbation, counterfactual generation, and adversarial attacks to probe explanation validity. These efforts are crucial for building trust in AI systems and ensuring their responsible deployment, particularly in high-stakes applications where understanding model behavior is paramount.