Generative A.I. models have emerged as versatile tools across diverse industries, with applications in privacy-preserving data sharing, computational art, personalization of products and services, and immersive entertainment. Here, we introduce a new privacy concern in the adoption and use of generative A.I. models: that of coincidental generation, where a generative model's output is similar enough to an existing entity, beyond those represented in the dataset used to train the model, to be mistaken for it. Consider, for example, synthetic portrait generators, which are today deployed in commercial applications such as virtual modeling agencies and synthetic stock photography. Due to the low intrinsic dimensionality of human face perception, every synthetically generated face will coincidentally resemble an actual person. Such examples of coincidental generation all but guarantee the misappropriation of likeness and expose organizations that use generative A.I. to legal and regulatory risk.

翻译：生成式人工智能模型已成为跨行业的多用途工具，应用于隐私保护数据共享、计算艺术、产品与服务个性化以及沉浸式娱乐等领域。本文提出生成式人工智能模型在应用与推广中的一项新隐私问题：即"偶然生成"现象——指模型生成的输出与某个现有实体（尤其是不在训练数据集中的实体）高度相似，以至于可能被误认为该实体。以目前商业化部署的虚拟模特代理公司和合成图库中使用的合成肖像生成器为例，由于人类面部感知的内在维度较低，每张合成人脸都会偶然地与真实人物相似。这类偶然生成实例几乎必然导致肖像权盗用，使采用生成式人工智能的组织面临法律与合规风险。