In “Digitally Divided,” Katherine Behar presents her artwork with a focus on how algorithms dismantle and rearrange us. Across culture, algorithms have been unleashed to allocate complex systems into manageable portions. They mete out standardization and suppress idiosyncrasy across diverse and defiant populations of human and nonhuman objects, in ways that are socially, technically, and conceptually reductive. This lecture brings together examples of Behar’s videos, interactive installations, sculptures, and performances, alongside episodes from media history and popular culture to explore this core notion of being “digitally divided.”

About the Speaker:
Katherine Behar is an artist and critical theorist of new media whose work explores gender and labor in digital culture. In contexts spanning automated labor, mandated obsolescence, big data, and machine learning, Behar applies object-oriented feminism into practice in her art and writing. Her work connects feminist and antiracist post-colonial histories with a wave of new theories that grapple with the nonhuman object world. Katherine Behar's works have appeared throughout North America and Europe. Pera Museum in Istanbul presented a comprehensive survey exhibition and catalog, Katherine Behar: Data's Entry | Veri Girişi, in 2016. Additional solo exhibitions include Katherine Behar: Anonymous Autonomous (2018), Katherine Behar: E-Waste (2014, catalog/traveling), and numerous others collaborating as "Disorientalism." Behar is the editor of Object-Oriented Feminism, coeditor of And Another Thing: Nonanthropocentrism and Art, and author of Bigger than You: Big Data and Obesity. She is Associate Professor of New Media Arts at Baruch College, CUNY.

The CRITICAL x DESIGN series is generously supported by the School of Information; the Center for Political Studies at the Institute for Social Research; the Science, Technology and Society program, and the Department of Communication Studies in the College of Literature, Science, and the Arts at the University of Michigan.