GT-free-XAI: A Ground Truth-Free XAI Framework for Decision Interpretation and Evaluation

Explainable Artificial Intelligence (XAI) for visual tasks remains a critical and challenging problem due to the lack of ground truth (GT) datasets, which limits the development of XAI evaluation methodologies. To address this issue, and inspired by two approaches, using either the model's output data (including model predictions and interpretable outputs) or utilizing Large Vision Language Models (LVLMs) to generate pseudo-ground truth, we propose a GT-free XAI evaluation method to assess visual task interpretations without the need for ground truth. We have developed a framework called GT-free XAI, which provides unique XAI evaluation capabilities for visual task. Preliminary experimental results demonstrate that the GT-free XAI evaluation method correctly evaluates the interpretations of different XAI methods, reduces the workload associated with manual labeling, and opens new directions for XAI evaluation.