arXiv cs.AI· Siyi Yu·· 12 小时前AI 评分26
研究：枯燥、肮脏、危险的工作条件与职业 AI 暴露度的关系
Dull, Dirty, Dangerous? Working Conditions and the Limits of Occupational AI Exposure
AI 导读
一项基于 754 个美国职业、21 项 O*NET 指标的研究发现，LLM 暴露度与 AIOE 两项指标下，AI 暴露度越高的工作，其枯燥、肮脏、危险条件评分越低。控制体力需求、教育、培训经验和工资后，LLM 暴露度的复合关联置信区间包含零，AIOE 仍低于零。将重复性与受限自主权分开，也会改变合并"Dull"分数的含义。
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