VirtualXAI: A User-Centric Framework for Explainability Assessment Leveraging GPT-Generated Personas

Understand

In today's data-driven era, computational systems generate vast amounts of data that drive the digital transformation of industries, where Artificial Intelligence (AI) plays a key role.

• Currently, the demand for eXplainable AI (XAI) has increased to enhance the interpretability, transparency, and trustworthiness of AI models.
• However, evaluating XAI methods remains challenging: existing evaluation frameworks typically focus on quantitative properties such as fidelity, consistency, and stability without taking into account qualitative characteristics such as satisfaction and interpretability.
• In addition, practitioners face a lack of guidance in selecting appropriate datasets, AI models, and XAI methods -a major hurdle in human-AI collaboration.