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:生成型AI模型已经成为各个行业中的通用工具,在隐私保护数据共享、计算艺术、产品和服务的个性化以及沉浸式娱乐方面都有应用。本文介绍了在采用和使用生成型AI模型时的一个新的隐私关注点:巧合生成,即一个生成模型的输出与现有实体相似度可能足够高,超出训练模型所使用的数据集所代表的范围,被误认为是它。例如,人工肖像生成器,其已适用于商业应用,如虚拟模特机构和合成股票摄影等。由于人脸感知的固有维度较低,每个合成人脸将巧合地类似于实际人。这些巧合生成的实例几乎保证了形象的误用,并曝露了使用生成型AI的组织面临的法律和监管风险。