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TroublingGAN


custom StyleGAN model, generated visuals, 2021






TroublingGAN is a critical generative artwork and artistic research project that experiments with the StyleGAN vector-image generative neural network.



It is a custom StyleGAN model trained on a heterogeneous dataset with images that reflect the current "troubling times" of our society. It generates new images based on what the algorithm has learned from a given image dataset - in this case, "new problems". Instead of images of human faces, the dataset consists of uncategorized media photos documenting the year 2020. 




Based on the thesis that transformative creative practices are needed to change the course of events and stop the re-creation of multiple causes of the current "problematic times", this work was created for the UROBOROS 2021 festival and was incorporated into the visuals of the 2nd edition of the festival.



This project is a critique of techno-solutionism and the false hope in artificial intelligence to solve our human problems. The romanticised notion of artificial intelligence as the saviour of humanity is easily challenged by the very nature of neural networks, which are merely statistical pattern recognition models to generate new examples of processed data. In other words,if we don't change the way we think about this world, and the ways we design for this world, we will keep repeating the same mistakes and generating new versions of the same problems.


The goal of this project is to question the predictive nature of generative neural networks and their ability to "understand" what we might call the "essence" of a monothematic but heterogeneous dataset of photographic images.

On the other hand, this project also tells us about how we look at images of various disasters in the media. If we remove the concrete content from the image and see only the "troublingness", we may be able to think more deeply about the visualized topic. These semi-abstract images do not grab attention with specific details. Because of the absence of these details, we are able to see recognized patterns in the troubling events and perhaps find a different perspective on them.