Edgelands

‍2018–
AI Generated Images/Videos
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Edgelands explores the increasing tension between the natural world and the infiltration of electronic waste. Electronic waste (e-waste) is the fastest growing waste stream on the planet. While 70% of new technology is recyclable, only 30% of it actually gets recycled. As devices increasingly get smaller and more advanced, their ability to be recycled drastically decreases due largely to custom fabrication techniques. This is leading to an enormous amount of material blanketing the surface of the earth and, worse, a culture of hazardous extraction practices in illegal e-waste dumpsites. Rare earth minerals—which are expensive and intensive to extract—end up serving far shorter lives as useful materials than they should. This puts the planet on the edge of a situation where finding solutions to extract materials from existing products will soon outvalue and outperform the process of digging into the earth to extract new materials.

This body of work investigates the ramifications of our capitalism-driven desire for the newest and best alongside the environmental crisis to which these discarding behaviors lead. Edgelands is a research project into technology using technology. The project speculatively explores this situation through machine learning—“breeding” images of midwestern landscapes with images of illegal e-waste dumpsites in Africa, Asia, and India. The resulting trained neural network hypothesizes a world where the mass of discarded electronics creeps into the periphery of everyday life and occupies the spaces abandoned by previous industries. The output from this newly trained model speculates on what this future might look like should we continue on the current trajectory. The images are simultaneously familiar and foreign, present and future, and encourage viewers to rethink their relationships to technology, devices, and the lifespan of said products.