From archive to dataset. Visualizing the latency of big data

The objective of the proposal is to analyze what latent space is within a Deep-Learning system and how its visualization is capable of triggering a meaning-effect concerning the epistemology of big data. The latent space is the mathematical space that maps what a Neural Network has learned from the training dataset. It is the result of the compression of the input data and the step before the Neural Network’s output, a step that usually remains invisible to the human eye, rendering effective the promise of a transparent effect of reality generally promoted by Artificial Intelligence technologies. Precisely in contrast with this promise, the visualization of this complex spatiality makes accessible, and therefore intelligible, the epistemic and rhetorical relations inscribed within datasets, intended as archives that gather information. To achieve my objective, I will consider an artistic project realized by multimedia artist and coder Jake Elwes, Zizi-Queering the Dataset (2019), a multi-channel video where different facial portraits are shown in a morphing loop that visualizes what a Generative Adversarial Network has learned from the re-training of a dataset containing portraits with another one containing facial images of drag and non-binary individuals. This artistic gesture has led to a series of epistemic issues concerning big data and their situated and ideological meaning.