Edgelands: Using Creative Technology to Predict the Future

A call to action for technology users, producers, and regulators

Jonathan Hanahan
Assistant Professor
Washington University in St. Louis

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 and no industry recycling or extraction standard. This dilemma 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 is a call to action for technology users, producers, and regulators regarding the ramifications of our capitalism driven desire for the newest and best alongside the global epidemic these discarding behaviors lead to. Edgelands is a research project in 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 quantity of discarded electronics creeps into the periphery of everyday life and occupies the spaces abandoned by previous industries. The resulting output 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 aspire to encourage viewers to rethink their relationships to technology, devices, and the lifespan of said products.

This research was presented at the Design Incubation Colloquium 7.2: 109th CAA Annual Conference on Wednesday, February 10, 2021.

What Can Machine Learning Contribute to Empathy in Design? How to Build a Journey Map Using Big Data and Text Sentiment Analysis

Sarah Pagliaccio
Principal, User Experience Designer
Black Pepper

What can machine learning contribute to empathy in design? How to build a journey map using big data and text sentiment analysis.

Art and design are meant to reflect the world around us, show empathy for those we design for, and reflect the emotional state of our customers and target users. But how are we meant to empathize with situations that are unfamiliar or out of context? What happens when we over-empathize and project our own emotional states on our customers’ experiences? That’s where machine learning comes in. With enough input, we can use machine learning tools, specifically text sentiment analysis, to provide an objective score of our users’ emotional experiences. By feeding transcripts of customer interviews into a computer, we can remove our own subjectivity from our analysis and form a holistic picture of others’ needs and wants.

These sentiment scores can turn words into pictures, emotions into graphs, expanding our understanding of design goals and tasks.

Using Shakespeare’s A Midsummer Night’s Dream as a case study, we will talk through the emotional journey, i.e., the customer journey map, of major characters in the play using text sentiment analysis. A discussion of how these techniques can be applied to consumer application and website design will follow.

This research was presented at the Design Incubation Colloquium 5.1: DePaul University on October 27, 2018.