Mind mapping three high-concept projects
Three artists whose work inspires me (described in my last post)
Mona Chalabi: data illustrations of social issues
Malika Favre: digital illustrations featuring geometric shapes and bold colors; centers women
Hannah Beachler: building worlds and technologies in a fictional, utopian universe
At first, I had a hard time connecting these artists with any kind of domains to research. Each of them primarily work in strictly visual environments, whether it’s pure visual art, data, or film production. But as I started to write out some potential domains for research, a few themes emerged.
Tech, bias, and racial inequality: Mona Chalabi uses data to expose social inequity, often related to race. Through her illustrations, she points out that data itself is often skewed—whether due to the collector of the data, or the criteria for the data. This leads to data visualizations just being accepted as fact rather than questioned. In DT, we look at data often through the lens of machine learning. How do our own biases affect the data that we are using to create AI?
Hannah Beachler doesn’t use technology to create her work, but she does imagine technology in her work. For Black Panther, she conceptualized the utopia of Wakanda, the most technologically advanced country in the world. It also happens to be a country that centers people of color. At first I looked at this through that same lens: how we can use future mapping to achieve racial equity. But I realized that we can use the same logic to apply to a different issue: climate change. What prospective technologies can I imagine that might help address the climate crisis?
Colors, shapes, and emotions: Mona Chalabi and Malika Favre both inspire me from a visual point of view, but if I look a deeper, it’s because they use colors and shapes in an effective way to invoke emotions. How can I utilize technology to reproduce those feelings? Projection mapping seems like a good tool for this.
Three domains to research
Prejudice in machine learning
Sci-fi prototyping for climate change
Projection mapping emotions using color
If you Google “racial prejudice in machine learning,” you get hundreds of hits. Horror stories of Google Photos identifying black people as gorillas, soap dispensers that don’t recognize dark skin, airport facial recognition technology providing false matches for people of color. It’s certainly well-documented, and good people are working on improving on it. But this will always be an issue, because the people working on the AI, no matter how good their intentions, have bias. There will never be a truly neutral algorithm. I’m interested in exploring how to best demonstrate that these “ooh-aah” technologies come from somewhere: a regular human with regular bias.
The organization Radical Ocean Futures uses science fiction prototyping to imagine how climate change might affect oceans and fisheries. Further still, the organization has come up with ways humans might adapt to these dramatically shifting environments. But climate change isn’t in the future; it’s here now. And despite trying to be an environmentally conscious consumer, it’s my belief that the biggest impact on climate change now is through the industries who are perpetuating climate change. I want to use future prototyping to drive activism against these companies.
Emotions and color are already inextricably linked, so I want to think about how to push that relationship further. When we feel a strong emotion, it can be all-consuming. Think of shame: when I feel embarrassed, I feel waves of heat rippling through me. We could represent that visually with actual waves of crimson shame creeping up on you. I want to artificially reproduce those emotions and make you feel those things simply through visual (and perhaps audio) stimuli.
Reverse-engineering AI to expose bias in the programmer
Creating a speculative object to put pressure on large companies perpetuating the climate crisis
Using color and geometric shapes, reproduce strong emotions through projection mapping
Truth be told, it’s a little hard for me to move from concept to project. But if we are truly doing research through making, I can hone in my concepts on very specific ideas to start testing my theories.
Looking at failures in facial recognition technology for Middle Eastern people through testing, then working backwards to identify the problems in the dataset
Sci fi prototyping to imagine a dystopia where tech companies don’t reduce carbon emissions; and a utopia where they do
Reproducing the feeling of shame through audio and visual mapping