A rapidly growing number of voices have argued that AI research, and computer
vision in particular, is closely tied to mass surveillance. Yet the direct path
from computer vision research to surveillance has remained obscured and
difficult to assess. This study reveals the Surveillance AI pipeline. We obtain
three decades of computer vision research papers and downstream patents (more
than 20,000 documents) and present a rich qualitative and quantitative
analysis. This analysis exposes the nature and extent of the Surveillance AI
pipeline, its institutional roots and evolution, and ongoing patterns of
obfuscation. We first perform an in-depth content analysis of computer vision
papers and downstream patents, identifying and quantifying key features and the
many, often subtly expressed, forms of surveillance that appear. On the basis
of this analysis, we present a topology of Surveillance AI that characterizes
the prevalent targeting of human data, practices of data transferal, and
institutional data use. We find stark evidence of close ties between computer
vision and surveillance. The majority (68%) of annotated computer vision papers
and patents self-report their technology enables data extraction about human
bodies and body parts and even more (90%) enable data extraction about humans
in general