Can "fake faces" Lead to the Illusion of Diversity?

Artificial intelligence is presenting considerable advancements in the generation of convincing and fascinating images of unquestionably realistic features that are almost impossible to be classified as belonging to people who do not exist anywhere in the world. If we compare the faces produced no more than five years ago and those published recently, the improvements are incredible. With the new GANs -- Generative Adversarial Networks, algorithmic architectures that use two neural networks, competing one against the other (thus the term "adversarial") to generate new, synthetic instances of data, these synthetic faces are easily customizable and editable, making them so credible thanks to particular effects. Applying a mix of features from real images such as the shades of the skin and the color of the hair, for example, to the fake ones is possible to generate a virtual population that does not exist in the real world. But why the use of images of non-existent people, created by an algorithm, instead of photos of real people can be so exciting?