Automated Generation of Explanations for Multimodal Generative AI Systems

This paper explores methods for automatically generating human-understandable explanations for decisions made by complex multimodal generative AI systems. It addresses the crucial need for explainable AI in applications involving various data types like images, text, and audio, aiming to increase transparency and trust in AI-driven outputs. This is vital for deploying such advanced AI in sensitive real-world applications where understanding the reasoning behind AI's output is paramount.