A Questionnaire on Art and Machine Learning

Abstract

Abstract Earlier this year, October distributed a questionnaire about art and machine learning to a select group of artists, art historians, critics, and curators, posing the following questions: “If AI algorithms have the capacity to analyze massive datasets and identify patterns, how has this capability influenced the generation of new artistic concepts and ideas? How are artists collaborating with, changing, torquing, or critiquing AI systems? Compared to, say, the history of photography and chance, or art and systems, does artists' use of generative AI today represent a difference in degree or kind? How have artists probed the creative possibilities of generative AI? How have they condemned the biases, ecological impact, and military-industrial origins of AI? Developed their own hybrid models and architectures? Explored the instrumentalization of AI systems, or, on the other hand, their unpredictability? What other models are possible? What is no longer possible? What is human or machine, creativity or computation, in the first place?” Responses were provided by K Allado-McDowell, American Artist, Nancy Baker-Cahill, Ian Cheng, Kate Crawford, Stephanie Dinkins, Simon Denny, Michele Elam, Noam Elcott, Alex Galloway, Holly Herndon and Matt Dryer, Tishan Hsu, David Joselit, Alexander Kluge, Lev Manovich, Trevor Paglen, Christiane Paul, Kris Paulsen, Warren Sack and Jennifer Gonzalez, Edward Shanken, Antonio Somaini, Christopher Kulendran Thomas, Fred Turner, and Amelia Winger-Bearskin.