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Ethical Testing in the Real World: Evaluating Physical Testing of Adversarial Machine Learning
This paper critically assesses the adequacy and representativeness of physical domain testing for various adversarial machine learning (ML) attacks against computer vision systems involving human subjects.
Computers and Society
Legal Risks of Adversarial Machine Learning Research
Adversarial Machine Learning is booming with ML researchers increasingly targeting commercial ML systems such as those used in Facebook, Tesla, Microsoft, IBM, Google to demonstrate vulnerabilities.
Politics of Adversarial Machine Learning
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.
