A growing body of literature has focused on understanding and addressing workplace AI design failures. However, past work has largely overlooked the role of the devaluation of worker expertise in shaping the dynamics of AI development and deployment. In this paper, we examine the case of feminized labor: a class of devalued occupations historically misnomered as ``women's work,'' such as social work, K-12 teaching, and home healthcare. Drawing on literature on AI deployments in feminized labor contexts, we conceptualize AI Failure Loops: a set of interwoven, socio-technical failure modes that help explain how the systemic devaluation of workers' expertise negatively impacts, and is impacted by, AI design, evaluation, and governance practices. These failures demonstrate how misjudgments on the automatability of workers' skills can lead to AI deployments that fail to bring value to workers and, instead, further diminish the visibility of workers' expertise. We discuss research and design implications for workplace AI, especially for devalued occupations.

翻译：越来越多的文献聚焦于理解和解决工作场所人工智能设计失败的问题。然而，以往的研究在很大程度上忽视了工人专业知识贬值在塑造人工智能开发与部署动态过程中的作用。本文以女性化劳动为例进行研究：这是一类历史上被误称为“女性工作”的贬值职业，例如社会工作、K-12教学和家庭医疗护理。借鉴在女性化劳动背景下人工智能部署的相关文献，我们提出了“人工智能失败循环”的概念：这是一组相互交织的社会技术性失败模式，有助于解释工人专业知识的系统性贬值如何对人工智能的设计、评估和治理实践产生负面影响，并反过来受其影响。这些失败案例表明，对工人技能可自动化程度的误判，可能导致人工智能的部署不仅未能为工人带来价值，反而进一步削弱了工人专业知识的可见性。我们讨论了工作场所人工智能，特别是针对贬值职业的研究与设计启示。