During the Fall of 2020, I had the honor of teaching a new course at the University of California in Los Angeles (UCLA) Department of Design Media Arts (DMA) entitled “Cultural Appropriation with Machine Learning”. This provocatively titled course came together after wrestling with many questions I had that year in the wake of the pandemic, black civil rights movements, and a crushed economy.

Rather than teach a course that focuses purely on the “how” of machine learning, like Creative Applications of Deep Learning does, I wanted to also include a critical component to guide students through the questions they should be asking as they learn and employ these tools. I also wanted students to understand how these tools and algorithms came to be in today’s society, so that they knew better what questions to ask when they were using them. It became clear early on that cultural appropriation was a central theme across most generative arts practices. I say this because machine learning requires large amounts of data which tend to come from existing corpora of creative content, such as flickr archives, or instagram collections. What does it mean when an algorithm owned by Google or Microsoft is capable … Continue reading...