How We Teach Algorithms Identity Morgan Klaus Scheuerman
Race and gender have long sociopolitical histories of classification in technical infrastructures-from the passport to social media. Facial analysis technologies are particularly pertinent to understanding how identity is operationalized in new technical systems. This talk will cover two studies on gender and race representations in facial analysis technology. First, I sought to understand how gender is concretely conceptualized and encoded into commercial facial analysis and image labeling technologies available today. Findings show how gender is codified as a binary into both classifiers and data standards. Second, I examined how race and gender are defined and annotated in image databases used for training and evaluating models. Though race and gender decisions are rarely justified or defined, they are discussed as apolitical, obvious, and neutral. These two studies show not only how race and gender are represented in facial analysis models, but also the value-decisions made at the data level and how inclusion and exclusion of certain identities are propagated through the model pipeline.
BIO: Morgan Klaus Scheuerman is a PhD Student of 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 recent work explores how gender and race classification in computer vision technologies excludes and endangers at-risk individuals.