Content policy and access limitations on commercial neural networks as an incentive to artivism

Abstract

This article employs a case‐study method to investigate the artivism neural network community concentrated on Twitter (since renamed X), which has been ideologically influenced by the content policy and limitations of OpenAI. Today, many young artists using machine learning technologies in their artworks (Midjourney, Stable Diffusion, Kandinsky) note that despite significant progress in the field of neural network generators of image through prompts present in museums and exhibitions of contemporary digital art, a significant number of artworks are still made chiefly using outdated text-to-image algorithms created in 2021. These neural networks continue to be popular in art to this day. The reasons for the sustainability of such practices can be found in the soft ideological conflict between artists and OpenAI in 2021. At that time, neural networks had not yet become mainstream, and the dominant theme was deep fakes, which became the basis for a comprehensive discussion about the possibilities and consequences of implementing AI algorithms in modern society. A series of scandals related to the work of neural networks alerted businesses, which feared the reputational costs of neural network errors and biases. At the same time, the existing discourse on freedom of speech, thought, and self-expression in contemporary art has led to ideological conflict, as the creators have introduced constraints on tools of artistic expression. Previously, the actions of artists were not moderated by technical means. Thus, the community did not accept this state of affairs, and as a result of cooperation and “collective intelligence” created, on the GitHub and Google Colab platforms, their own algorithms with open code, with which everyone could carry out their visual experiments. Artists face the ideological question of fighting globalism and anti-progress in art to be outside the system but to riot against it. This process led to a division of artistic practises in neural network art, outlined by media artist Ryan Murdock as a gateway to text-guided visual art by the hacker effort of 2021 or the modern generation of algorithm text-to-images (after 2022).