生成式AI助力就业：国际劳工组织报告揭示其对岗位的影响

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标题："【202308】国际劳工组织-ChatGPT等生成式AI对就业的影响：数量与质量的全球分析(2)_55页_英文.pdf" 该研究报告是由Paweł Gmyrek、Janine Berg和David Bescond三位作者在2023年8月发布的一份国际劳工组织工作论文。这份报告名为"Generative AI and Jobs: A Global Analysis of Potential Effects on Job Quantity and Quality"，深入探讨了生成式人工智能（如ChatGPT）等技术对劳动力市场的影响，特别关注它们对就业机会的数量和质量可能带来的变化。报告的核心观点是，尽管生成式AI在某些任务上展现出强大的自动化能力，但它并不意味着将全面取代现有的工作岗位。相反，它被视作一种工具，可以增强而非消除就业。报告通过全球视角分析，强调了AI技术如何通过提高生产效率、创造新的职业领域、以及提升技能需求来推动就业市场的发展。首先，AI可以自动化重复性和低技能的工作，从而释放人力资源，让员工有更多机会从事更复杂、更具创新性和价值增值的任务。这可能会导致某些岗位减少，但同时也会催生出新的职位，如AI项目经理、数据科学家、机器学习工程师等。其次，报告指出，随着AI技术的应用，对于教育和培训的需求会增加，因为劳动力需要掌握与AI协同工作的新技能。这意味着一个持续学习和适应新技术的职业环境，从而保持就业竞争力。然而，报告也警告说，如果社会政策和教育体系没有跟上AI发展的步伐，可能会出现技能不匹配的问题，即部分劳动者可能找不到与他们现有技能相匹配的工作。因此，政策制定者需要采取措施，确保劳动力市场能够有效应对这种转型。最后，报告还提到了公平性和包容性问题，AI的广泛应用应当避免加剧社会经济不平等，特别是对于那些容易受到技术冲击的群体，如低技能工人和较小经济体中的工作者，需要提供相应的支持和转型机会。这份报告通过对生成式AI潜在影响的深入分析，提出了一个既充满挑战又充满机遇的未来就业景象。它强调了协作、教育改革和技术政策在确保AI技术促进就业增长和可持续发展方面的重要性。

ILO Working Paper 96

Introduction

Each new wave of technological progress intensies debates on automation and jobs. Current

debates on Articial Intelligence (AI) and jobs recall those of the early 1900s with the introduc-

tion of the moving assembly line, or even those of the 1950s and 1960s, which followed the intro-

duction of the early mainframe computers. While there have been some nods to the alienation

that technology can bring by standardizing and controlling work processes, in most cases, the

debates have centred on two opposing viewpoints: the optimists, who view new technology as

the means to relieve workers from the most arduous tasks, and the pessimists, who raise alarm

about the imminent threat to jobs and the risk of mass unemployment.

What has changed in debates on technology and workers, however, is the types of workers af-

fected. While the advances in technology in the early, mid and even late-1900s were primarily

focused on manual workers, technological development since the 2010s, in particular the rapid

progress of Machine Learning (ML), has centred on the ability of computers to perform non-rou-

tine, cognitive tasks, and by consequence potentially aect white-collar or knowledge workers.

In addition, these technological advancements have occurred in the context of much strong-

er interconnectedness of economies across the globe, leading to a potentially larger exposure

than location-based, factory-level applications. Yet despite these developments, to an average

worker, even in the most highly developed countries, the potential implications of AI have, until

recently, remained largely abstract.

The launch of ChatGPT marked an important advance in the public’s exposure to AI tools. In this

new wave of technological transformation, machine learning models have started to leave the

labs and begin interacting with the public, demonstrating their strengths and weaknesses in

daily use. The chat function dramatically shortened the distance between AI and the end user,

simultaneously providing a platform for a wide range of custom-made applications and inno-

vations. Given these signicant advancements, it is not surprising that concerns over potential

job loss have resurged.

While it is impossible to predict how generative AI will further develop, the current capabilities

and future potential of this technology are central to discussions of its impact on jobs. Sceptics

tend to believe that these machines are nothing more than “stochastic parrots” – powerful text

summarizers, incapable of “learning” and producing original content, with little future for gen-

eral purpose use and unsustainable computing costs (Bender et al. 2021). On the other hand,

more recent technical literature focused on testing the limits of the latest models suggests an

increasing capability to carry out “novel and dicult tasks that span mathematics, coding, vision,

medicine, law, psychology and more”, and a general ability to produce responses exhibiting some

forms of early “reasoning” (Bubeck et al. 2023). Some assessments go as far as suggesting that

machine learning models, especially those based on large neural networks used by Generative

Pre-trained Transformers (GPT, see Text Box 1), might have the potential to eventually become a

general-purpose technology (Goldfarb, Taska, and Teodoridis 2023; Eloundou et al. 2023).

This

would have multiplier eects on the economy and labour markets, as new products and servic-

es would likely spring from this technological platform.

As social scientists, we are not in position to take sides in these technical debates. Instead, we

focus on the already demonstrated capabilities of GPT-4, including custom-made chatbots with

retrieval of private content (such as collections documents, e-mails and other material), natu-

ral language processing functions of content extraction, preparation of summaries, automated

content generation, semantic text searches and broader semantic analysis based on text em-

beddings. Large Language Models (LLMs) can also be combined with other ML models, such as

The three main characteristics of general-purpose technologies are pervasiveness, ability to continue improving over time, and abil-

ity to spawn further innovation (Jovanovic and Rousseau, 2005).

ILO Working Paper 96

speech-to-text and text-to-speech generation, potentially expanding their interaction with dif-

ferent types of human tasks. Finally, the potential of interacting with live web content through

custom agents and plugins, as well as the multimodal (not exclusive to text, but also capable of

reading and generating image) character of GPT-4 makes it likely that this type of technology

will expand into new areas, thereby increasing its impact on labour.

Departing from these observations, this study seeks to add the global perspective to the already

lively debate on possible changes that may result in the labour markets as a consequence of the

recent advent of generative AI. We stress the focus of our work on the concepts of “exposure”

and “potential”, which does not imply automation, but rather lists occupations and associated

employment gures for jobs that are more likely to be aected by GPT-4 and similar technologies

in the coming years. The objective of this exercise is not to derive headline gures, but rather to

analyse the direction of possible changes in order to facilitate the design of appropriate policy

responses, including the possible consequences on job quality.

The analysis is based on 4-digit occupational classications and their corresponding tasks in the

ISCO-08 standard. It uses the GPT-4 model to estimate occupational and task-level scores of ex-

posure to GPT technology and subsequently links these scores to ocial ILO statistics to derive

global employment estimates. We also apply embedding-based text analysis and semantic clus-

tering algorithms to provide a better understanding of the types of tasks that have a high auto-

mation potential and discuss how the automating and augmenting eects will strongly depend

on a range of additional factors and specic country context.

We discuss the results of this analysis in the broader context of labour market transformations.

We put particular focus on the current disparities in digital access across countries of dierent

income levels, the potential for this new wave of technological transformation to aggravate such

disparities, and the ensuing consequences on productivity and income. We also give consider-

ation to jobs with highest automation and augmentation potential and discuss gender-specic

dierences. The analysis does not take into account the new jobs that will be created to accom-

pany the technological advancement. Twenty years ago, there were no social media managers,

thirty years ago there were few web designers, and no amount of data modelling would have

rendered a priori predictions concerning a vast array of other occupations that have emerged

in the past decades. As demonstrated by Autor et al. (2022), some 60 per cent of employment in

2018 in the United States was in jobs that did not exist in the 1940s.

Indeed, the main value of studies such as this one is not in the precise estimates, but rather in

understanding the possible direction of change. Such insights are necessary for proactively de-

signing policies that can support orderly, fair, and consultative transitions, rather than dealing

with change in a reactive manner. For this reason, we also emphasize the potential eects of

technological change on working conditions and job quality and the need for workplace consul-

tation and regulation to support the creation of quality employment and to manage transitions

in the labour market.

We hope that this research will contribute to needed policy debates on digital transformation in

the world of work. While the analysis outlines potential implications for dierent occupational

categories, the outcomes of the technological transition are not pre-determined. It is humans

that are behind the decision to incorporate such technologies and it is humans that need to

guide the transition process. It is our hope that this information can support the development

of policies needed to manage these changes for the benet of current and future societies. We

intend to use this broad global study as an opening to more in-depth analyses at country level,

with a particular focus on developing countries.

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