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This conversation between scholar and poet Tung-Hui Hu and artist Elisa Giardina Papa addresses new forms of precarious labor emerging within artificial intelligence economies. Together they examine a global infrastructure of low-paid human microworkers who “clean” data and train machine vision algorithms, labelling, categorizing, annotating, and validating massive quantities of visual data. Hu and Giardina Papa discuss methods and psychological theories underpinning affective computing in the context of Giardina Papa’s latest art project, which explores the labor of producing and cleansing data sets of human expressions. A number of AI systems that supposedly recognize, interpret, and simulate human affects base their algorithms on flawed understandings of human emotions as universal, authentic, and transparent. Increasingly, tech companies and American government agencies like the Transportation Security Administration are leveraging this supposed transparency to develop software that identifies, on the one hand, consumers’ moods and, on the other hand, potentially dangerous airline passengers. In this exchange, Hu and Giardina Papa consider both the historical and present-day implications of this demand for legibility and transparency.