Attacks from adversarial machine learning (ML) have the potential to be used
"for good": they can be used to run counter to the existing power structures
within ML, creating breathing space for those who would otherwise be the
targets of surveillance and control. But most research on adversarial ML has
not engaged in developing tools for resistance against ML systems. Why? In this
paper, we review the broader impact statements that adversarial ML researchers
wrote as part of their NeurIPS 2020 papers and assess the assumptions that
authors have about the goals of their work. We also collect information about
how authors view their work's impact more generally. We find that most
adversarial ML researchers at NeurIPS hold two fundamental assumptions that
will make it difficult for them to consider socially beneficial uses of
attacks: (1) it is desirable to make systems robust, independent of context,
and (2) attackers of systems are normatively bad and defenders of systems are
normatively good. That is, despite their expressed and supposed neutrality,
most adversarial ML researchers believe that the goal of their work is to
secure systems, making it difficult to conceptualize and build tools for
disrupting the status quo.

本篇论文研究了逆向机器学习的社会影响，发现大多数逆向机器学习研究者的观点和假设会导致他们难以考虑到攻击工具的社会益处和构建工具打破主流秩序。