This paper introduces the IRIS Benchmark, a novel framework designed to synchronously evaluate fairness in both understanding and generation tasks within Unified Multimodal Large Language Models (UMLLMs). It aims to resolve the "Tower of Babel" dilemma of fragmented fairness metrics by offering a multi-dimensional, trade-off analysis approach and uncovering systemic biases in leading models.