Effective Automation to Support the Human Infrastructure in AI Red Teaming
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910897681727488 |
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| author | Zhang, Alice Qian Suh, Jina Gray, Mary L. Shen, Hong |
| author_facet | Zhang, Alice Qian Suh, Jina Gray, Mary L. Shen, Hong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22116 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Effective Automation to Support the Human Infrastructure in AI Red Teaming Zhang, Alice Qian Suh, Jina Gray, Mary L. Shen, Hong Computers and Society Human-Computer Interaction 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. |
| title | Effective Automation to Support the Human Infrastructure in AI Red Teaming |
| topic | Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.22116 |