Search
Browse By Day
Browse By Person
Browse By Room
Browse By Research Area
Browse By Session Type
Search Tips
Meeting Home Page
Personal Schedule
Sign In
This presentation analyzes Google SafeSearch’s use of machine vision to automate censorship. First I 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 five 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 labelers and examine the possibility for automatically outing ‘closeted’ LGBTIQ people. Third, I examine instances of Google censoring non-pornographic content and the adjudication mechanisms Google offers for redress. Fourth, I look at instances where explicitness is blurry, focusing on Google understanding the term ‘bisexuality’ as indicative of pornography. Fifth, 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.