Politics of Adversarial Machine Learning
It highlights a critical societal problem for policymakers and researchers by exposing how adversarial ML can be misused in authoritarian contexts.
The paper examines the political implications of adversarial machine learning, showing how attacks and defenses can impact civil liberties and human rights, with real-world examples illustrating risks like suppressing dissent.
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