Albert, Delano, Penney, Rigot & Kumar on Evaluating Physical Testing of Adversarial Machine Learning

Kendra Albert (Harvard Law School), Maggie Delano (Swarthmore College Engineering Department), Jon Penney (Citizen Lab, University of Toronto, Harvard University – Berkman Klein Center for Internet & Society, Harvard Law School), Afsaneh Rigot (ARTICLE 19), and Ram Shankar Siva Kumar, Microsoft Corporation, Harvard University – Berkman Klein Center for Internet & Society have posted “Ethical Testing in the Real World: Evaluating Physical Testing of Adversarial Machine Learning” to SSRN. Here is the abstract:

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. Many papers that deploy such attacks characterize themselves as “real world.” Despite this framing, however, we found the physical or real-world testing conducted was minimal, provided few details about testing subjects and was often conducted as an afterthought or demonstration. Adversarial ML research without representative trials or testing is an ethical, scientific, and health/safety issue that can cause real harms. We introduce the problem and our methodology, and then critique the physical domain testing methodologies employed by papers in the field. We then explore various barriers to more inclusive physical testing in adversarial ML and offer recommendations to improve such testing notwithstanding these challenges.