Like almost all civil discourse today, the conversation around machine learning and artificial intelligence is incredibly polarizing—especially within universities—where a mixed message exists around its ethical, practical, and necessary use. Everyone is confused about how to deal with it. I am witness to this day-to-day in an art school context as artists-in-training grapple with how to incorporate these new powerful tools into their own practices, or alternatively, reject and police them for themselves and others. I’ve heard more than a few anecdotal stories of art students’ work being attacked at critiques and even defaced when suspicions and debate arise over the use of AI in the creative process. While at the other extreme, students in art and design programs that prioritize strictly commercial or entertainment end-use of the creative process are encouraged to take up the tools of generative AI with little regard for the ethical, environmental, or artistic implications. It is quite simply the wild west out there and little is being done to slow down the speed of change and pose important questions.
Looking back at my own research interests around technology, I am struck by how much my thinking has been framed within the context of power relationships and the desire to contextualize new media forms within specific historical and socio-political moments that artists and audiences experienced in lived time and real space. Whether it is examining the role that photography and early film played as a new means of representation precisely at the moment of the historical avant-garde’s period of experimentation in the late 19th to early 20th centuries, or, more recently, unpacking how the expansion of NFT and AI art coincides with the rise of populism, global neoliberalism, and the influence of the billionaire class in the realm of visual culture, the striking part has more to do with my assumption that a healthy dose of criticality exists with most humanities researchers of my generation when it comes to understanding and theorizing the role of new technology. Put more simply, nothing is ever really “new” when it comes to technology, especially when you see it from a historical perspective shaped by human-engineered relationships of power versus knowledge and notions of liberty (a big shout out here to all the Foucault I absorbed in the late 90s and early oughts that brought me to this place).
This fall I will try to do my part to slow down and make space for reflection by teaching a special topics undergraduate course that brings some clarity and critical understanding to the ways and means through which machine learning is currently shaping contemporary art and visual culture. In preparing for this course, I spent the past several months assembling a set of readings that will inform the core questions of my syllabus. I am sharing the reading list and core questions below both in an effort to encourage the consideration of these ideas as key to any artist, art historian, or visual arts researcher interested in the critical study of art in the age of AI, but also to highlight the important work being done in the field represented by the four books I have used to bring this course together.
I hope to report back at the end of the semester about what I learned teaching this course, and I will be on the lookout for others in my field taking on this topic as a much-needed addition to the art school curriculum in the years to come.
SUGGESTED READING LIST:
John Maeda, How to Speak Machine: A Gentle Introduction to Artificial Intelligence. MIT Press, 2025.
This is the book I first encountered some years ago that helped me understand the core principles of machine learning systems through easy-to-understand principles. Now in a newly updated edition, the book draws on Maeda’s pragmatic and interdisciplinary approach to unpacking how computing technology works. In my course, the six chapters of How to Speak Machine set up a conceptual frame that helps students identify the form and content of how machines operate to set in tension with the artistic process.
Sofian Audry, Art in the Age of Machine Learning. MIT Press, 2021.
Audry’s book is at the heart of my course and brings together years of research and many examples of artists and art practices that exist at the intersection of machine learning and new media art. While dense and geared towards more graduate level discussion, the structure of the book is incredibly useful to frame many of the most important core questions that relate directly to contemporary art and curatorial practice in my course. And even though Audry is less focused on traditional art practices, the book’s arguments and theories help students rethink the idea that machine learning has little impact on artists working outside new media or digital art.
Lev Manovich and Emanuele Arielli. "Artificial Aesthetics." 2024.
Manovich is arguably the most important, innovative, and interdisciplinary theorist/artist/art historian/philosopher working on the field of new media, digital culture, and the emerging field of AI aesthetics, and this book of essays is at the cutting edge of critical thinking around generative AI. I’ve been reading and using Manovich’s theories to frame my own understanding of new media technologies for over 15 years, and his accessible, elegant, and even humorous approach to theorizing technology is a key underpinning in my course. In particular, the essay “Who is an artist in AI era?” should be required reading for all creatives and will ground early debates in my course. Manovich also makes almost all his writing openly accessible on his website, so I encourage artists and researchers to access and share his publications widely.
Nicholas Carr, Superbloom, How Technologies of Connection Tear Us Apart. WW Norton, 2025
The last book is a bit of a wildcard addition to the construction of my course, but a necessary human element that cuts through the academic conversation around machine learning, computers, and new technologies. Some may recall Carr’s 2010 book The Shallows: What the Internet is Doing to Our Brains, and the important conversations it introduced around the changing dynamics of how humans think, act, and live when exposed to new technologies. Many have compared Carr to Marshall McLuhan, and I am using this book to update many of the ideas McLuhan introduced in the 1960s that predicted the digital age in which we currently live.
CORE QUESTIONS ADDRESSED IN MY COURSE:
These are the ideas and specific weekly core questions (tied to the learning outcomes) driving my course as outlined on my syllabus. They have been created through a synthesis of the four texts outlined above and will provide a framework through weekly classes.
What are some of the myths and misconceptions around machine learning and artistic practice?
What is the role of creativity in the era of software, social media, and “content creation?”
How are art practices similar and different from those of engineering and scientific practices?
How do artists hijack the training process?
What are the aesthetic properties of adaptive behaviours?
What is black-box computing and how can artists take advantage of the lack of visible processes present in AI?
How do different machine learning systems align with different kinds of artistic practices?
How can artists work with “shallow” and “deep” learning models?
How can artists use data as a raw material to shape machine learning systems and the creative process?
How does remix culture connect artists to the world of AI algorithms?
What is the impact of machine learning on the art world and curatorial practices? What are the sociopolitical implications of machine learning art in the 21st century?