arbiter
JournalsPricingAccount

AI Red-Teaming Is a Sociotechnical Problem

Tarleton Gillespie et al.

Communications of the ACM2026https://doi.org/10.1145/3731657preprint
AJG 2
Weight
0.37

What the paper says

As generative AI technologies find more and more real-world applications, the importance of testing their performance and safety is paramount. “Red-teaming” has quickly become the primary approach to testing AI models—prioritized by AI companies, and enshrined in AI policy and regulation. Members of red teams act as adversaries, probing AI systems to test their safety mechanisms and uncover vulnerabilities. Yet we know far too little about this work or its implications. In this article, we highlight the importance of understanding the values and assumptions behind red-teaming, the labor arrangements involved, and the psychological impacts on red-teamers, drawing insights from lessons learned around the work of content moderation. Red-teaming should be a deeply interdisciplinary concern. To avoid repeating the mistakes of the recent past, we call for a coordinated network of scholars, from the full range of the computational and social sciences, to study the technical, social, critical, and policy dimensions of red-teaming and of the emerging sociotechnical system that is AI.

1 citation

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1145/3731657

Or copy a formatted citation

@article{tarleton2026,
  title        = {{AI Red-Teaming Is a Sociotechnical Problem}},
  author       = {Tarleton Gillespie et al.},
  journal      = {Communications of the ACM},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1145/3731657},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

AI Red-Teaming Is a Sociotechnical Problem

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.