  In addition to their security properties, adversarial machine-learning
attacks and defenses have political dimensions. They enable or foreclose
certain options for both the subjects of the machine learning systems and for
those who deploy them, creating risks for civil liberties and human rights. In
this paper, we draw on insights from science and technology studies,
anthropology, and human rights literature, to inform how defenses against
adversarial attacks can be used to suppress dissent and limit attempts to
investigate machine learning systems. To make this concrete, we use real-world
examples of how attacks such as perturbation, model inversion, or membership
inference can be used for socially desirable ends. Although the predictions of
this analysis may seem dire, there is hope. Efforts to address human rights
concerns in the commercial spyware industry provide guidance for similar
measures to ensure ML systems serve democratic, not authoritarian ends

Computers and Society;Cryptography and Security;Machine Learning;Machine Learning