# Effective Automation to Support the Human Infrastructure in AI Red Teaming

arXiv: https://arxiv.org/abs/2503.22116 · HTML: https://mlsanity.com/paper/2503.22116/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2025.

## Built on by (most cited first, 3 of 3)

- [PersonaTeaming: Exploring How Introducing Personas Can Improve Automated AI Red-Teaming](https://mlsanity.com/paper/2509.03728/) (2025)
- [A Systematic Review of Algorithmic Red Teaming Methodologies for Assurance and Security of AI Applications](https://mlsanity.com/paper/2602.21267/) (2026)
- [Red Teaming LLMs as Socio-Technical Practice: From Exploration and Data Creation to Evaluation](https://mlsanity.com/paper/2602.18483/) (2026)

## Abstract (arXiv)

As artificial intelligence (AI) systems become increasingly embedded in critical societal functions, the need for robust red teaming methodologies continues to grow. In this forum piece, we examine emerging approaches to automating AI red teaming, with a particular focus on how the application of automated methods affects human-driven efforts. We discuss the role of labor in automated red teaming processes, the benefits and limitations of automation, and its broader implications for AI safety and labor practices. Drawing on existing frameworks and case studies, we argue for a balanced approach that combines human expertise with automated tools to strengthen AI risk assessment. Finally, we highlight key challenges in scaling automated red teaming, including considerations around worker proficiency, agency, and context-awareness.