Data, Technology, and Gender : Thinking About (and From) Trans Lives

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50 Years of Test (Un)fairness: Lessons for Machine Learning (2018, background): “er (although received attention later, e.g., [67]). The role of culture in gender identity and gender presentation has seen less consideration in ML fairness, but gender labels raise ethical concerns [31, 33]. Comparable to modern sentiment in the difficulties of measuring fairness, earlier decision”
This Thing Called Fairness (2019, background): “empirical analysis to design, including: critical analysis of harms such systems can cause (particularly when purportedly recognizing and categorizing gender) and the violence they can perpetuate [18, 65, 75]; ethnographic investigation of how data science and algorithmic work is taught, learned, an”