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Envisioning Identity: The Social Production of Computer Vision

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Speaker: Morgan Klaus Scheuerman, Postdoctoral Associate, Information Science, University of Colorado Boulder


Abstract: Computer vision technologies have been increasingly scrutinized in recent years for their propensity to cause harm. Broadly, the harms of computer vision focus on demographic biases (favoring one group over another) and categorical injustices (through erasure, stereotyping, or problematic labels). Prior work has focused on both uncovering these harms and mitigating them, through, for example, better dataset collection practices and guidelines for more contextual data labeling. There is opportunity to further understand how human identity is embedded into computer vision not only across these artifacts, but also across the network of human workers who shape computer vision systems. Further, given computer vision is designed by humans, there is ample opportunity to understand how human positionality influences the outcomes of computer vision systems. In this talk, I present work on how identity is implemented in computer vision, from how identity is represented in models and datasets to how different worker positionalities influence the development process. Specifically, I showcase how representations of gender and race in computer vision are exclusionary, and represent problematic histories present in colonialist worldviews. I also highlight how traditional tech workers enact a positional power over data workers in the global south. Through these findings, I demonstrate how identity in computer vision moves from something more open, contextual, and exploratory to a completely closed, binary and prescriptive classification.

Bio: Morgan Klaus Scheuerman is a Postdoctoral Associate in Information Science at University of Colorado Boulder and a 2021 MSR Research Fellow. His research focuses on the intersection of technical infrastructure and marginalized identities. In particular, he examines how gender and race characteristics are embedded into algorithmic infrastructures and how those permeations influence the entire system. His work has received multiple best paper awards and honorable mentions at CHI and CSCW. He earned his MS degree in Human-Centered Computing from University of Maryland Baltimore County and his BA in Communication & Media Studies (Minor Gender & Sexuality Studies) from Goucher College.