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.

翻译：随着人工智能系统日益嵌入关键社会功能，对稳健红队测试方法的需求持续增长。本文探讨了AI红队测试自动化的新兴方法，特别关注自动化方法的应用如何影响人力驱动的测试工作。我们讨论了自动化红队测试流程中的劳动角色、自动化的优势与局限，及其对AI安全和劳动实践的广泛影响。基于现有框架和案例研究，我们主张采用结合人类专业知识与自动化工具的平衡方法，以加强AI风险评估。最后，我们指出了扩展自动化红队测试面临的关键挑战，包括对工作者熟练度、自主权和情境感知等方面的考量。