With significant advances in generative AI, new technologies are rapidly
being deployed with generative components. Generative models are typically
trained on large datasets, resulting in model behaviors that can mimic the
worst of the content in the training data. Responsible deployment of generative
technologies requires content moderation strategies, such as safety input and
output filters. Here, we provide a theoretical framework for conceptualizing
responsible content moderation of text-to-image generative technologies,
including a demonstration of how to empirically measure the constructs we
enumerate. We define and distinguish the concepts of safety, fairness, and
metric equity, and enumerate example harms that can come in each domain. We
then provide a demonstration of how the defined harms can be quantified. We
conclude with a summary of how the style of harms quantification we demonstrate
enables data-driven content moderation decisions.

本研究提出了一个理论框架来对文本到图像生成技术进行负责任的内容管理，其中包括了定义并区分了安全、公平和度量公平的概念，并且详细列举了可能出现的伤害案例，并提供了如何量化所定义的危害的实证演示，用数据驱动的内容管理决策来实现量化危害的操作。