Rapid progress in general-purpose AI has sparked significant interest in "red
teaming," a practice of adversarial testing originating in military and
cybersecurity applications. AI red teaming raises many questions about the
human factor, such as how red teamers are selected, biases and blindspots in
how tests are conducted, and harmful content's psychological effects on red
teamers. A growing body of HCI and CSCW literature examines related
practices-including data labeling, content moderation, and algorithmic
auditing. However, few, if any, have investigated red teaming itself. This
workshop seeks to consider the conceptual and empirical challenges associated
with this practice, often rendered opaque by non-disclosure agreements. Future
studies may explore topics ranging from fairness to mental health and other
areas of potential harm. We aim to facilitate a community of researchers and
practitioners who can begin to meet these challenges with creativity,
innovation, and thoughtful reflection.

人工智能红队测试引起了很多有关人的因素的问题，如红队成员的选择、测试中的偏见和盲点，以及有害内容对红队员的心理影响。这篇研究探讨了相关实践的概念和实证挑战，并旨在促进一个能够创造性、创新性和深思熟虑地应对这些挑战的研究者和实践者社群。

人在 AI 红队中的因素：社交与协作计算的视角

The Human Factor in AI Red Teaming: Perspectives from Social and  Collaborative Computing

Cobweb, a human like category learning system, differs from other incremental
categorization models in constructing hierarchically organized cognitive
tree-like structures using the category utility measure. Prior studies have
shown that Cobweb can capture psychological effects such as the basic level,
typicality, and fan effects. However, a broader evaluation of Cobweb as a model
of human categorization remains lacking. The current study addresses this gap.
It establishes Cobweb's alignment with classical human category learning
effects. It also explores Cobweb's flexibility to exhibit both exemplar and
prototype like learning within a single model. These findings set the stage for
future research on Cobweb as a comprehensive model of human category learning.

Cobweb 是一种类似于人类的类别学习系统，通过使用类别效用度量构建具有层级组织的认知树状结构。该研究证明了 Cobweb 能够捕捉到基层、典型性和粉丝效应等心理效应，从而填补了之前对 Cobweb 作为人类类别学习模型的综合评估的缺失。同时，它还探索了 Cobweb 在一个模型中能够同时表现出示例和原型化学习的灵活性。这些发现为进一步研究 Cobweb 作为人类类别学习的综合模型奠定了基础。