Critical AI

This issue provides an overview of the emerging interdisciplinary field of Critical AI, which seeks to demystify artificial intelligence; counter its mythologizing as a marvelous and impenetrable black box; and translate, interpret, and critique its operations, from data collection and model architecture to decision making. Artists and researchers are developing new methods, practices, and concepts for this critical project, which is both historicist and attentive to the institutional, technological, and epistemic transformations still underway. Contributors to this special issue collectively articulate and evince just such a critical approach to AI, one that combines humanistic and technical inquiry in its exploration of disciplinary and epistemological questions on the one hand, and the techniques of machine learning on the other. Featured contributions articulate some of the social, cultural, and ethicopolitical dimensions of machine learning in domains such as ecologies, art, poetics, race, warfare, pedagogy, and speculative fiction.

Contributors: Ranjodh Singh Dhaliwal, Evan Donahue, Michele Elam, Seb Franklin, Christopher Grobe, N. Katherine Hayles, Tung-Hui Hu, Patrick Jagoda, Melody Jue, Fabian Offert, Rita Raley, Jennifer Rhee, R. Joshua Scannell, J.D. Schnepf, Tyler Shoemaker, Avery Slater, Luke Stark, Lindsay Thomas, Sherryl Vint