UCLA 2020
Introduction
This course guides students through state-of-the-art methods for generative content generation in machine learning (ML) with a special focus on developing a critical understanding surrounding its usage in creative practices. We begin by framing our understanding through the critical lens of cultural appropriation. Next, we look at how machine learning methods have enabled artists to create digital media of increasingly uncanny realism aided by larger and larger magnitudes of cultural data, leading to new aesthetic practices but also new concerns and difficult questions of authorship, ownership, and ethical usage.
Course
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”.
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 of recreating any artistic content? What of the artist, then? And what happens when the tools for creation are placed in the hands of someone who generates from this algorithm content of another culture? These were some of the questions we wrestled with while collecting data for our algorithms and working with off-the-shelf algorithms already trained on large archives.
I have made most of the lecture content available freely online on youtube.com. The lecture slides are also linked in the video descriptions, and the end of each lecture has information on the homework. Not shown are the discussion sessions and homework presentations where the students presented and engaged with the content through their own voice. As well, any interaction with the students during the lectures was edited out to protect their identity. Lastly, the guest lecturer, Holly Grimm, a Diné (Navajo) artist based in Santa Fe, New Mexico working with Machine Learning practices is also not shown in this series.