研究分析枯燥、脏污、危险职业与 AI 暴露度的关联及局限
Dull, Dirty, Dangerous? Working Conditions and the Limits of Occupational AI Exposure
AI 导读
一项 arXiv 研究利用 O*NET 的 21 个条目分析美国 754 种职业中“枯燥、脏污、危险”工作条件与职业 AI 暴露度的关系。在大语言模型度量与 AI Occupational Exposure（AIOE）两种度量下，较高暴露度均与较低工作条件评分相关，等权与就业加权结果一致；控制体力要求、教育、培训和工资后，LLM 暴露的置信区间包含零，AIOE 仍低于零。
正文 · 原文
Abstract:Automation is often justified as relief from dull, dirty, and dangerous work. I examine how these conditions relate to occupational AI exposure using 21 O*NET items across 754 US occupations. Higher exposure is associated with lower condition scores under a large language model (LLM) measure and AI Occupational Exposure (AIOE), with equal occupation weights and employment weights. Adjustment for physical demands, required education, training and experience, and wages reduces the composite association. The adjusted confidence intervals include zero for LLM exposure and remain below zero for AIOE. Separating repetition from restricted discretion also changes what the combined Dull score conveys. These comparisons show how item groupings and occupational characteristics shape the interpretation of exposure estimates. Occupational profiles offer a starting point for selecting contrasting research settings. Establishing desired automation or realized relief requires evidence about workers' preferences and specific interventions.
| Subjects: | Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); General Economics (econ.GN) |
| Cite as: | arXiv:2611.06888 [cs.AI] |
| (or arXiv:2611.06888v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06888 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Siyi Yu [view email]
[v1]
Wed, 4 Nov 2026 18:41:40 UTC (92 KB)
来源：arXiv cs.AI · arxiv.org