Human-computer image generation using Generative Adversarial Networks (GANs) is becoming a well-established methodology for casual entertainment and open artistic exploration. Here, we take the interaction a step further by weaving in carefully structured design elements to transform the activity of ML-assisted imaged generation into a catalyst for large-scale popular dialogue on complex socioscientific problems such as the United Nations Sustainable Development Goals (SDGs) and as a gateway for public participation in research.
@article{arxiv.2010.12324,
title = {The power of pictures: using ML assisted image generation to engage the crowd in complex socioscientific problems},
author = {Janet Rafner and Lotte Philipsen and Sebastian Risi and Joel Simon and Jacob Sherson},
journal= {arXiv preprint arXiv:2010.12324},
year = {2020}
}