This article deals with how images produced using artificial intelligence-based algorithms differ from existing photography. A generative adversarial network (GAN) is an algorithm that calculates fake data through balanced learning of generators and discriminators. When using original photos as learning data, it creates images that are visually indistinguishable from photos. There are two issues raised by this. First, the algorithmic image cannot be called a photograph from the point of view of index theory, but it is no different from the existing digital photograph in that it creates a new image by changing the pixel value of the original photograph. Second, the algorithmic image is an advanced form of program automatism and human exclusion, which Vilem Flusser defined as the core of the technical image. Humans are excluded from the production process of the GAN algorithm image. In that sense, the generator of the GAN is a black box. As the automaticity of the program increases, humans do not control image production and become simple consumers. Therefore, it is time for humans to think about how to turn a black box called a program into a “transparent box.”