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.

翻译：越来越多的声音认为，人工智能研究，特别是计算机视觉，与大规模监控密切相关。然而，从计算机视觉研究到监控的直接路径仍被掩盖且难以评估。本研究揭示了监控人工智能管道的全貌。我们获取了三十年的计算机视觉研究论文及其下游专利（超过两万份文献），并进行了丰富的定性与定量分析。该分析揭示了监控人工智能管道的性质与范围、其制度根源与演变进程，以及持续存在的模糊化模式。我们首先对计算机视觉论文及下游专利进行了深度内容分析，识别并量化了关键特征及大量常以微妙方式呈现的监控形式。基于此分析，我们提出了监控人工智能的拓扑结构，刻画了其对人数据的普遍定向、数据转移实践及制度性数据使用。我们发现了计算机视觉与监控之间存在紧密联系的显著证据。大多数（68%）经标注的计算机视觉论文与专利自行报告其技术能够实现对人体及身体部位的数据提取，而更高比例（90%）的技术则普遍支持对人类的数据提取。