mlsanityData to 7 Oct 2026

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

arXiv 2407.07786 ↗First posted 10 Jul 2024cs.HC cs.AI cs.CYcited by 8

On the map: LLM safety and security → Prompt injection and agent security

Abstract (arXiv)

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. 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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security. Builds on 3 of its 25 references that are in the map. First posted on arXiv 10 Jul 2024.

Builds on

3 of 25 references.

References · 3

  1. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  2. 2021Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?cited by 11
  3. 2023Responsible AI Research Needs Impact Statements Toocited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 2; 2026: 2.

8 papers.