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The power of pictures: using ML assisted image generation to engage the crowd in complex socioscientific problems

Authors :
Rafner, Janet
Philipsen, Lotte
Risi, Sebastian
Simon, Joel
Sherson, Jacob
Publication Year :
2020

Abstract

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.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2010.12324
Document Type :
Working Paper