  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.
