TroublingGAN: generated visual ambiguity as a speculative alternative to photojournalism

This exposition documents artistic research that engages with generative neural networks and artificial intelligence-driven visual synthesis, the goal being to challenge the limits of the research and question the value of the generated visual outcomes. We present here our experiment with a customised StyleGAN model. In contrast to its utilisation by computer scientists, it has been trained on a heterogeneous dataset, voluntarily exposing the generative neural network to failure while focusing on the unexpected moments of surprise that arise from such a process. Led by the critical thoughts of The Nooscope Manifested (Pasquinelli and Joler 2020), this experiment questions what kind of knowledge generative neural networks produce, whether they could change one’s perspective on studied objects (the dataset) and whether they could function as a viable tool for artistic research. The observed object in this project is the concept of ‘troubling times’ (inspired by ‘Designing in Troubling Times’, the theme of the 2021 Uroboros Festival) — rapid socio-ecological changes caused by shifts in global economic, political and technological power and the subsequent series of troubling events, including the COVID-19 pandemic, violent conflicts and environmental catastrophes. StyleGAN is used as a pattern-recognition and knowledge-production tool to create an intuitive understanding of what this ambiguous term ‘troubling times’ actually means today. During the experimentation process, multiple unforeseen moments and twists happened, offering valuable insights into the nature of synthetic media, generating ethical questions concerning the use of generative neural networks and spawning new propositions for further research.