人工智能:加剧还是缓解失业?

IF 10.1 1区 社会学 Q1 SOCIAL ISSUES Technology in Society Pub Date : 2024-11-06 DOI:10.1016/j.techsoc.2024.102755
Meng Qin , Yue Wan , Junyi Dou , Chi Wei Su
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引用次数: 0

摘要

人工智能(AI)的快速发展在促进新工作机会激增的同时,也使一些传统角色过时,特定技能落伍。以往的研究没有考虑人工智能对失业影响的短期、中期和长期变化,这可能导致对人工智能与就业关系的理解不全面。本文研究了 2013 年 1 月 4 日至 2024 年 8 月 12 日的每日数据,利用先进的基于小波的量子回归(QQR)方法,评估了人工智能对失业指数(UI)在量子和时间尺度上的影响,样本量为 2820 个,取自更大的数据集,共 4241 个观测值。结论显示,由于自动化取代工人的速度快于新工作角色的出现和技能的转变,人工智能在短期内通常会对失业指数产生积极影响,尤其是在人工智能处于 0.6-0.7 量级时。然而,从中期来看,随着新的工作岗位和技能在不断的产业结构调整中涌现,正面和负面影响趋于平衡。从长期来看,人工智能主要是通过进一步促进经济发展、推动技能升级和促进市场调整来缓解失业率,但在人工智能达到 0.7 量级和失业率达到最高量级(如 2019 年冠状病毒病(COVID-19))时,这一结果并不成立。在新技术革命和产业转型的背景下,我们从短期、中期、长期和具体行业的角度提出了中国避免人工智能引发潜在失业危机的具体建议。
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Artificial Intelligence: Intensifying or mitigating unemployment?
The rapid development of Artificial Intelligence (AI) is simultaneously fostering a proliferation of novel job opportunities while rendering some traditional roles obsolete and specific skills outdated. Previous research has failed to consider the short-, medium-, and long-term variations in AI's impact on unemployment, which may lead to an incomplete understanding of the AI-employment relationship. This paper examines daily data from January 4, 2013, to August 12, 2024, utilising advanced wavelet-based Quantile on Quantile Regression (QQR) methodology to assess AI's impact on the Unemployment Index (UI) across quantiles and time scales, with a sample size of 2820 drawn from a larger dataset totalling 4241 observations. The conclusions reveal that AI generally positively impacts UI in the short term, especially with AI at 0.6–0.7 quantiles, as automation replaces workers faster than new job roles emerge and skills transform. However, in the medium term, positive and negative effects balance as new jobs and skills emerge through continuous industrial restructuring. In the long run, AI predominantly mitigates UI by further enhancing economic development, fostering skill upgrading, and facilitating market adjustments, but this result does not hold during AI at 0.7 quantiles and UI at the highest quantiles, such as Coronavirus Disease 2019 (COVID-19). Under new technological revolution and industrial transformation, we formulate China-specific suggestions to avert potential AI-induced unemployment crisis from short-term, medium-term, long-term, and sector-specific perspectives.
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来源期刊
CiteScore
17.90
自引率
14.10%
发文量
316
审稿时长
60 days
期刊介绍: Technology in Society is a global journal dedicated to fostering discourse at the crossroads of technological change and the social, economic, business, and philosophical transformation of our world. The journal aims to provide scholarly contributions that empower decision-makers to thoughtfully and intentionally navigate the decisions shaping this dynamic landscape. A common thread across these fields is the role of technology in society, influencing economic, political, and cultural dynamics. Scholarly work in Technology in Society delves into the social forces shaping technological decisions and the societal choices regarding technology use. This encompasses scholarly and theoretical approaches (history and philosophy of science and technology, technology forecasting, economic growth, and policy, ethics), applied approaches (business innovation, technology management, legal and engineering), and developmental perspectives (technology transfer, technology assessment, and economic development). Detailed information about the journal's aims and scope on specific topics can be found in Technology in Society Briefings, accessible via our Special Issues and Article Collections.
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