We cannot assume that just because something is data driven or processed by an algorithm it is immune to bias. Labels and categories we may take for granted need to be interrogated. The more we know about the histories of racial categorization, the more we learn about how a variety of cultures approach gender, the deeper we dive into the development of scientific scales like the Fitzpatrick scale, the easier it is to see the human touch that shapes AI systems. Instead of erasing our fingerprints from the creation of algorithmic systems, exposing them more clearly gives us a better
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