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This article analyzes Google SafeSearch's use of machine vision to automate censorship. I first provide a history of SafeSearch, followed by an overview of Google's machine vision system and the datasets it is built atop. Next I examine its political implications from four perspectives. First, I demonstrate how English-language semantic biases about sexuality are embedded in WordNet's ontology. Second, I outline how ImageNet embeds the biases of both image makers and labellers and examine the possibility for automatically outing ‘closeted’ LGBTQIA+ people. Third, I look at instances where explicitness is blurry, focusing on Google understanding the term ‘bisexuality’ as indicative of pornography. Lastly, I analyze the post-2012, always-on SafeSearch model that requires explicit keywords to trigger pornographic results, which reifies mainstream heteroporn's dominance online. In closing, I suggest some tactics of resistance.

I know it when I see it: the heteronormativity of Google's SafeSearch