Digital Labor

Ayhan Ayteş


Ayhan Ayteş’s research focuses on cognitive labor, and cultural history of Artificial Intelligence in relation to subjectivity, temporality and ethnicity. His Ph.D. degree is in Communication and Cognitive Science from the University of California, San Diego and his research informs and is informed by conversations between Media Studies, Cultural Studies, and Histories of Science and Technology, especially those focused on the interface between human cognition and media technologies. Ayhan’s digital media works have been exhibited in various venues including Istanbul Museum of the History of Science and Technology (permanent collection) and Aksanat Culture and Arts Center.

Reading Faces in the Crowd: Postcolonial Algorithms of Affective Computing
There has been a growing interest in implementing crowdsourcing technologies for affective computing problems. These cognitive labor apparatuses are a networked extension of what Bernard Stiegler calls “grammatization” through “cognitive and affective proletarianization.” I would like to contextualize networked grammatization of affect starting from the debate between Margaret Mead and Paul Ekman whose emotion classification system has been a central influence in contemporary affective computing applications, ranging from video surveillance systems to sentiment analysis of consumer reviews. In contrast to Margaret Mead, Paul Ekman suggests that human emotions are universal as there is no culture-specific aspect to facial expressions according to his study of “stone-age cultures in New Guinea.” Today, Ekman’s “universal” taxonomy has been reified into affective computing agents through various mechanisms including crowdsourcing platforms such as Amazon Mechanical Turk. This standardization takes crucial characteristics as most of the crowdsourcing platforms derive the required cognitive labor from a global work force. I argue that what is activated in these apparatuses of affective proleterianization is the postcolonial premise of locating global subject as a target of surveillance and control as well as a statistically predictable consumer/worker.