XAI Evaluation

Evaluating Explainable AI (XAI) methods focuses on developing robust and reliable metrics to assess the quality and trustworthiness of explanations generated by AI models. Current research emphasizes both quantitative metrics, such as faithfulness and stability, often applied to methods like Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM), and qualitative, user-centered approaches that consider factors like learning, utility, and engagement. This field is crucial for building trust in AI systems across diverse applications, from medical diagnosis to climate science, by ensuring that explanations are not only accurate but also understandable and useful to human users. The development of standardized benchmarks and comprehensive evaluation frameworks is a key area of ongoing work.