Toward trustworthy AI with integrative explainable AI frameworks

Abstract

As artificial intelligence (AI) increasingly permeates high-stakes domains such as healthcare, transportation, and law enforcement, ensuring its trustworthiness has become a critical challenge. This article proposes an integrative Explainable AI (XAI) framework to address the challenges of interpretability, explainability, interactivity, and robustness. By combining XAI methods, incorporating human-AI interaction and using suitable evaluation techniques, the implementation of this framework serves as a holistic XAI approach. The article discusses the framework's contribution to trustworthy AI and gives an outlook on open challenges related to interdisciplinary collaboration, AI generalization and AI evaluation.