Data forms the backbone of artificial intelligence (AI). Privacy and data protection laws thus have strong bearing on AI systems. Shielded by the rhetoric of compliance with data protection and privacy regulations, privacy-preserving techniques have enabled the extraction of more and new forms of data. We illustrate how the application of privacy-preserving techniques in the development of AI systems--from private set intersection as part of dataset curation to homomorphic encryption and federated learning as part of model computation--can further support surveillance infrastructure under the guise of regulatory permissibility. Finally, we propose technology and policy strategies to evaluate privacy-preserving techniques in light of the protections they actually confer. We conclude by highlighting the role that technologists could play in devising policies that combat surveillance AI technologies.

翻译：数据构成了人工智能（AI）的支柱。因此，隐私与数据保护法律对AI系统具有深远影响。在遵守数据保护与隐私法规的修辞掩护下，隐私保护技术反而促成了更多新型数据形式的提取。我们阐述了隐私保护技术在AI系统开发中的应用——从作为数据集整理环节的私有集合交集，到作为模型计算环节的同态加密与联邦学习——如何能在监管许可的幌子下进一步支撑监控基础设施。最后，我们提出了技术与政策策略，以基于隐私保护技术实际提供的保护效果来评估其效能。我们最终强调技术人员可在制定对抗监控型AI技术的政策中发挥关键作用。