国际劳工组织-ChatGPT等生成式AI能增强就业不会取代岗位（英）-2023-WN9.pdf

XGenerative AI and Jobs:A global analysis of potential effects on job quantity and qualityAuthors/Pawe Gmyrek,Janine Berg,David Bescond August/2023ILO Working Paper 96群内每日免费分享5份+最新资料 群内每日免费分享5份+最新资料 300T网盘资源+4040万份行业报告为您的创业、职场、商业、投资、亲子、网赚、艺术、健身、心理、个人成长 全面赋能！添加微信，备注“入群”立刻免费领取 立刻免费领取 200套知识地图+最新研报收钱文案、增长黑客、产品运营、品牌企划、营销战略、办公软件、会计财务、广告设计、摄影修图、视频剪辑、直播带货、电商运营、投资理财、汽车房产、餐饮烹饪、职场经验、演讲口才、风水命理、心理思维、恋爱情趣、美妆护肤、健身瘦身、格斗搏击、漫画手绘、声乐训练、自媒体打造、效率软件工具、游戏影音扫码先加好友，以备不时之需扫码先加好友，以备不时之需行业报告/思维导图/电子书/资讯情报行业报告/思维导图/电子书/资讯情报致终身学习者社群致终身学习者社群关注公众号获取更多资料关注公众号获取更多资料Copyright International Labour Organization 2023This is an open access work distributed under the Creative Commons Attribution 4.0 International License(https:/creativecommons.org/licenses/by/4.0/).Users can reuse,share,adapt and build upon the original work,as detailed in the License.The ILO must be clearly credited as the own-er of the original work.The use of the emblem of the ILO is not permitted in connection with users work.Attribution The work must be cited as follows:Gmyrek,P.,Berg,J.,Bescond,D.Generative AI and Jobs:A global analysis of potential effects on job quantity and quality.ILO Working Paper 96.Geneva:International Labour Office,2023.Translations In case of a translation of this work,the following disclaimer must be added along with the attribution:This translation was not created by the International Labour Organization(ILO)and should not be considered an official ILO translation.The ILO is not responsible for the content or accuracy of this translation.Adaptations In case of an adaptation of this work,the following disclaimer must be added along with the attribution:This is an adaptation of an original work by the International Labour Organization(ILO).Responsibility for the views and opinions expressed in the adaptation rests solely with the author or authors of the adaptation and are not endorsed by the ILO.This CC license does not apply to non-ILO copyright materials included in this publication.If the material is attributed to a third party,the user of such material is solely responsible for clearing the rights with the right holder.Any dispute arising under this license that cannot be settled amicably shall be referred to arbitra-tion in accordance with the Arbitration Rules of the United Nations Commission on International Trade Law(UNCITRAL).The parties shall be bound by any arbitration award rendered as a result of such arbitration as the final adjudication of such a dispute.All queries on rights and licensing should be addressed to the ILO Publishing Unit(Rights and Licensing),1211 Geneva 22,Switzerland,or by email to rightsilo.org.ISBN 9789220395356(print),ISBN 9789220395363(web PDF),ISBN 9789220395370(epub),ISBN 9789220395387(mobi),ISBN 9789220395394(html).ISSN 2708-3438(print),ISSN 2708-3446(digital)https:/doi.org/10.54394/FHEM8239 The designations employed in ILO publications,which are in conformity with United Nations practice,and the presentation of material therein do not imply the expression of any opinion whatsoever on the part of the ILO concerning the legal status of any country,area or territory or of its authorities,or concerning the delimitation of its frontiers.The responsibility for opinions expressed in signed articles,studies and other contributions rests solely with their authors,and publication does not constitute an endorsement by the ILO of the opinions expressed in them.Reference to names of firms and commercial products and processes does not imply their en-dorsement by the ILO,and any failure to mention a particular firm,commercial product or pro-cess is not a sign of disapproval.Information on ILO publications and digital products can be found at:www.ilo.org/publnsILO Working Papers summarize the results of ILO research in progress,and seek to stimulate discussion of a range of issues related to the world of work.Comments on this ILO Working Paper are welcome and can be sent to RESEARCHilo.org,bergilo.org.Authorization for publication:Richard Samans,Director RESEARCHILO Working Papers can be found at:www.ilo.org/global/publications/working-papersSuggested citation:Gmyrek,P.,Berg,J.,Bescond,D.2023.Generative AI and Jobs:A global analysis of potential ef-fects on job quantity and quality,ILO Working Paper 96(Geneva,ILO).https:/doi.org/10.54394/FHEM823901 ILO Working Paper 96AbstractThis study presents a global analysis of the potential exposure of occupations and tasks to Generative AI,and specifically to Generative Pre-Trained Transformers(GPTs),and the possible implications of such exposure for job quantity and quality.It uses the GPT-4 model to estimate task-level scores of potential exposure and then estimates potential employment effects at the global level as well as by country income group.Despite representing an upper-bound estimate of exposure,we find that only the broad occupation of clerical work is highly exposed to the tech-nology with 24 per cent of clerical tasks considered highly exposed and an additional 58 percent with medium-level exposure.For the other occupational groups,the greatest share of highly ex-posed tasks oscillates between 1 and 4 per cent,and medium exposed tasks do not exceed 25 per cent.As a result,the most important impact of the technology is likely to be of augmenting work automating some tasks within an occupation while leaving time for other duties as op-posed to fully automating occupations.The potential employment effects,whether augmenting or automating,vary widely across coun-try income groups,due to different occupational structures.In low-income countries,only 0.4 per cent of total employment is potentially exposed to automation effects,whereas in high-income countries the share rises to 5.5 percent.The effects are highly gendered,with more than double the share of women potentially affected by automation.The greater impact is from augmenta-tion,which has the potential to affect 10.4 percent of employment in low-income countries and 13.4 percent of employment in high-income countries.However,such effects do not consider infrastructure constraints,which will impede the possibility for use in lower-income countries and likely increase the productivity gap.We stress that the primary value of this analysis is not the precise estimates,but rather the in-sights that the overall distribution of such scores provides about the nature of possible changes.Such insights can encourage governments and social partners to proactively design policies that support orderly,fair,and consultative transitions,rather than dealing with change in a reactive manner.Moreover,the likely ramifications on job quality might be of greater consequence than the quantitative impacts,both with respect to the new jobs created because of the technology,but also the potential effects on work intensity and autonomy when the technology is integrat-ed into the workplace.For this reason,we also emphasize the need for social dialogue and reg-ulation to support quality employment.About the authorsPawe Gmyrek is Senior Researcher in the Research Department of the ILO.Janine Berg is Senior Economist in the Research Department of the ILO.David Bescond is Data Scientist in the ILOs Department of Statistics.02ILO Working Paper 96Abstract 01About the authors 01Acronyms 05 XIntroduction 07 X 1 Methods and Data 101.1.ISCO data on occupations and tasks 111.2.Prompt design and sequence 12 X 2 Assessment of the Predictions,Robustness Tests and the Bounds for Analysis 17 X 3 Results 203.1.Automation vs augmentation:distribution of scores across tasks and occupations 24 X 4 Exposed occupations as a share of employment:global and income-based estimates 304.1.Augmentation vs Automation:ILO microdata 304.2.Augmentation vs Automation:global estimate 324.3.The big unknown 36 X 5 Managing the transition:Policies to address automation,augmentation and the growing digital divide 385.1 Mitigating the negative effects of automation 385.2 Ensuring job quality under augmentation 395.3 Addressing the digital divide 40 XConclusion 43Appendix 1.Countries with missing ISCO-08 4-digit data:estimation procedure 45References 47Acknowledgements and use of GPT 51Table of contents03ILO Working Paper 96List of FiguresFigure 1.Mean automation scores by occupation,based on ISCO and GPT tasks 21Figure 2.Tasks with medium and high GPT-exposure,by occupational category(ISCO 1-digit)24Figure 3.Box plot of task-level scores by ISCO 4d,grouped by ISCO 1d 25Figure 4.Augmentation vs automation potential at occupational level 27Figure 5.Occupations with high automation potential 28Figure 6.Occupations with high augmentation potential 29Figure 7a.Automation vs augmentation potential:shares of total employment,microdata for 59 countries 30Figure 7b.Automation vs augmentation potential:shares of total employment in each sex(ILO microdata)31Figure 8.Country coverage based on the level of digits in ISCO-08(ILO data)33Figure 9a.Global estimates:jobs with augmentation and automation potential as share of total employment 34Figure 9b.Automation vs augmentation potential:shares of total employment for each sex(global estimate)35Figure 10.Occupations with high automation potential,by ISCO 4-digit and income group 36Figure 11a.The“Big Unknown”:occupations between augmentation and automation potential 37Figure 11b.The“Big Unknown”:share of total employment,by income group(global estimate)37Figure 11.Share of population not using the internet 41Figure 12.A classic growth path:income and occupational diversification 4204ILO Working Paper 96List of Tables 11 14 15 17 22 26Table 1.ISCO-08 Structure of occupations and tasks used in the study Table 2.Sample of tasks and definitions from ISCO and predicted by GPT-4 Table 3.Sample of task-level scores(high-income country context)Table 4.a Test of score consistency(100 task-level predictions)Table 4.b Tasks with high automation potential clustered into thematic groups*Table 5.Grouping of occupations based on task-level scores Table 6.Microdata coverage by levels ISCO-08:number of countries 3205 ILO Working Paper 96Acronyms3GThird Generation(referring to a generation of standards for mobile telecom-munications)AdaA language model by OpenAI used to generate embeddingsAGIArtificial General IntelligenceAIArtificial IntelligenceANNArtificial Neural NetworkAPIApplication Programming InterfaceATMsAutomated Teller MachinesCPUCentral Processing UnitDLDeep LearningDOLEDepartment of Labor and Employment ESCOEuropean Skills,Competences,Qualifications and OccupationsGPTsGenerative Pre-Trained TransformersGPT-4Generative Pre-Trained Transformer 4GPUGraphics Processing UnitHICHigh-Income CountriesICTInformation and Communications TechnologyILOInternational Labour OrganizationISCOInternational Standard Classification of OccupationsISCO-08International Standard Classification of Occupations 2008K-MeansK-Means Clustering AlgorithmLFSLabour Force SurveysLICLow-Income CountriesLLMsLarge Language Models06 ILO Working Paper 96LMICLower-Middle-Income CountriesMLMachine LearningNLPNatural Language ProcessingOECDOrganisation for Economic Co-operation and DevelopmentO*NETOccupational Information NetworkOpenAIOpen Artificial Intelligence(organizations name)PythonHigh-level programming languageRLReinforcement LearningSDStandard DeviationSMEsSmall and Medium-sized EnterprisesUMICUpper-Middle-Income CountriesUSUnited StatesUSDUnited States DollarUMICUpper-Middle-Income CountriesUSUnited States07 ILO Working Paper 96 XIntroductionEach new wave of technological progress intensifies debates on automation and jobs.Current debates on Artificial 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 affect 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 publics 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 significant 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 difficult 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).1 This would have multiplier effects 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 foc