人工智能暴露预测失业风险:一种新方法来应对技术驱动的失业
Morgan R Frank1,2,3,4, Yong-Yeol Ahn4,5, Esteban Moro3,4,6
1Department of Informatics and Networked Systems, University of Pittsburgh, Pittsburgh, PA 15216, USA.
PNAS nexus
|April 11, 2025
概括
人工智能 (AI) 可能会影响就业稳定,但标准就业数据不足以衡量失业风险. 使用职业特定失业数据的整体方法可以更准确地预测人工智能对工作中断的影响.
科学领域:
- 经济学 经济学 经济学
- 劳动研究 劳动研究
- 人工智能对影响的影响
背景情况:
- 公众对人工智能有可能破坏就业并增加失业的可能性存在担忧.
- 之前评估自动化风险的研究依赖于就业和工资统计数据,忽视了失业率的动态.
- 由于缺乏跨职业,地理和时间的全面数据,阻碍了对失业风险的直接审查.
研究的目的:
- 通过使用新的职业级失业数据,评估人工智能暴露模型,分职和失业动态.
- 引入一个新的指标",失业风险",以更准确地评估工作中断.
- 确定标准就业统计和个人人工智能暴露模型是否足够预测失业风险.
主要方法:
- 利用来自美国州失业保险办公室 (2010-2020) 的每月职业级失业数据.
- 开发并测试了针对失业风险的AI暴露模型,控制教育,技能,季节性和区域因素.
- 将个人AI暴露模型与预测能力的整体方法进行比较.
主要成果:
- 标准的就业统计数据对职业失业风险没有充分的代表性.
- 个别的人工智能暴露模型对失业风险,州失业率和分工率的预测能力不佳.
- 综合方法显著改善了预测,占失业风险变化的额外18%.
结论:
- 自动化在很大程度上影响了美国的失业率,其影响是复杂的.
- 职业特定的工作中断数据对于准确评估人工智能对未来工作的影响至关重要.
- 依靠单个人工智能暴露分数可能会误解人工智能对就业和失业的真正影响.
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