Abstract:
Facial recognition systems are increasingly common components of smartphones and other consumer digital devices. These technologies enable animated video-sharing applications, such as Apple’s animoji and memoji, Facebook Messenger’s masks and filters and Samsung’s AR Emoji. Such animations serve as technical phenomena translating moments of affective and emotional expression into mediated, trackable, and socially legible forms across a variety of social media platforms.
Through technical and historical analysis of these digital artifacts, the talk will explore the ways facial recognition systems classify and categorize racial identities in human faces in relation to emotional expression. Drawing on the longer history of discredited pseudosciences such as phrenology, the paper considers the dangers of both racializing logics as part of these systems of classification, and of how social media data regarding emotional expression gathered through these systems can be used to reinforce systems of oppression and discrimination.
Speaker Biography
Luke Stark is a Postdoctoral Researcher in the Fairness, Accountability, Transparency and Ethics (FATE) Group at Microsoft Research Montreal. His scholarship examines the history and contemporary effects of digital media used for social and emotional interaction; his work has been published in venues including Social Studies of Science, Media Culture and Society, History of the Human Sciences, and The International Journal of Communication. He has previously been a Postdoctoral Fellow in the Department of Sociology at Dartmouth College, a Fellow and Affiliate of the Berkman Klein Center for Internet & Society at Harvard University, and an inaugural Fellow with the University of California Berkeley’s Center for Technology, Society, and Policy. He holds a PhD from the Department of Media, Culture, and Communication at New York University, and an Honours BA and MA in History from the University of Toronto.
This talk is hosted by The Social Media Research Lab (SMRL)
Facial recognition systems are increasingly common components of smartphones and other consumer digital devices. These technologies enable animated video-sharing applications, such as Apple’s animoji and memoji, Facebook Messenger’s masks and filters and Samsung’s AR Emoji. Such animations serve as technical phenomena translating moments of affective and emotional expression into mediated, trackable, and socially legible forms across a variety of social media platforms.
Through technical and historical analysis of these digital artifacts, the talk will explore the ways facial recognition systems classify and categorize racial identities in human faces in relation to emotional expression. Drawing on the longer history of discredited pseudosciences such as phrenology, the paper considers the dangers of both racializing logics as part of these systems of classification, and of how social media data regarding emotional expression gathered through these systems can be used to reinforce systems of oppression and discrimination.
Speaker Biography
Luke Stark is a Postdoctoral Researcher in the Fairness, Accountability, Transparency and Ethics (FATE) Group at Microsoft Research Montreal. His scholarship examines the history and contemporary effects of digital media used for social and emotional interaction; his work has been published in venues including Social Studies of Science, Media Culture and Society, History of the Human Sciences, and The International Journal of Communication. He has previously been a Postdoctoral Fellow in the Department of Sociology at Dartmouth College, a Fellow and Affiliate of the Berkman Klein Center for Internet & Society at Harvard University, and an inaugural Fellow with the University of California Berkeley’s Center for Technology, Society, and Policy. He holds a PhD from the Department of Media, Culture, and Communication at New York University, and an Honours BA and MA in History from the University of Toronto.
This talk is hosted by The Social Media Research Lab (SMRL)
Explore Similar Events
-
Loading Similar Events...
