This workshop focuses on the foundational challenges of building AI systems that are unbiased, interpretable, and trustworthy. It aims to uncover the origins of algorithmic and data bias, advance the science of interpretability, and explore rigorous evaluation methods to ensure AI reliability. By bringing together researchers across biomedical imaging and signal processing, the workshop highlights novel methodologies and theoretical insights, emphasizing UIT as a scientific discipline rather than just an application concern. The event will showcase recent advances and foster discussions on future directions for inherently fair and transparent AI systems.