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ILOWorkingPaper96
August/2023
X
GenerativeAIandJobs:Aglobal
analysisofpotentialeffectsonjob
quantityandquality
Authors/PawełGmyrek,JanineBerg,DavidBescond
Copyright©InternationalLabourOrganization2023
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01ILOWorkingPaper96
Abstract
ThisstudypresentsaglobalanalysisofthepotentialexposureofoccupationsandtaskstoGenerativeAI,andspecificallytoGenerativePre-TrainedTransformers(GPTs),andthepossibleimplicationsofsuchexposureforjobquantityandquality.ItusestheGPT-4modeltoestimatetask-levelscoresofpotentialexposureandthenestimatespotentialemploymenteffectsatthegloballevelaswellasbycountryincomegroup.Despiterepresentinganupper-boundestimateofexposure,wefindthatonlythebroadoccupationofclericalworkishighlyexposedtothetech-nologywith24percentofclericaltasksconsideredhighlyexposedandanadditional58percentwithmedium-levelexposure.Fortheotheroccupationalgroups,thegreatestshareofhighlyex-posedtasksoscillatesbetween1and4percent,andmediumexposedtasksdonotexceed25percent.Asaresult,themostimportantimpactofthetechnologyislikelytobeofaugmentingwork–automatingsometaskswithinanoccupationwhileleavingtimeforotherduties–asop-posedtofullyautomatingoccupations.
Thepotentialemploymenteffects,whetheraugmentingorautomating,varywidelyacrosscoun-tryincomegroups,duetodifferentoccupationalstructures.Inlow-incomecountries,only0.4percentoftotalemploymentispotentiallyexposedtoautomationeffects,whereasinhigh-incomecountriesthesharerisesto5.5percent.Theeffectsarehighlygendered,withmorethandoubletheshareofwomenpotentiallyaffectedbyautomation.Thegreaterimpactisfromaugmenta-tion,whichhasthepotentialtoaffect10.4percentofemploymentinlow-incomecountriesand13.4percentofemploymentinhigh-incomecountries.However,sucheffectsdonotconsiderinfrastructureconstraints,whichwillimpedethepossibilityforuseinlower-incomecountriesandlikelyincreasetheproductivitygap.
Westressthattheprimaryvalueofthisanalysisisnotthepreciseestimates,butratherthein-sightsthattheoveralldistributionofsuchscoresprovidesaboutthenatureofpossiblechanges.Suchinsightscanencouragegovernmentsandsocialpartnerstoproactivelydesignpoliciesthatsupportorderly,fair,andconsultativetransitions,ratherthandealingwithchangeinareactivemanner.Moreover,thelikelyramificationsonjobqualitymightbeofgreaterconsequencethanthequantitativeimpacts,bothwithrespecttothenewjobscreatedbecauseofthetechnology,butalsothepotentialeffectsonworkintensityandautonomywhenthetechnologyisintegrat-edintotheworkplace.Forthisreason,wealsoemphasizetheneedforsocialdialogueandreg-ulationtosupportqualityemployment.
Abouttheauthors
PawełGmyrekisSeniorResearcherintheResearchDepartmentoftheILO.JanineBergisSeniorEconomistintheResearchDepartmentoftheILO.DavidBescondisDataScientistintheILO’sDepartmentofStatistics.
02ILOWorkingPaper96
Tableofcontents
Abstract
Abouttheauthors
Acronyms
01
01
05
X
Introduction
07
X
1
MethodsandData
1.1.ISCOdataonoccupationsandtasks
1.2.Promptdesignandsequence
10
11
12
X
2
AssessmentofthePredictions,RobustnessTestsandtheBoundsforAnalysis
17
X
3
Results
3.1.Automationvsaugmentation:distributionofscoresacrosstasksandoccupations
20
24
X
4
Exposedoccupationsasashareofemployment:globalandincome-basedestimates
4.1.AugmentationvsAutomation:ILOmicrodata
4.2.AugmentationvsAutomation:globalestimate
4.3.Thebigunknown
30
30
32
36
X
5
Managingthetransition:Policiestoaddressautomation,augmentationandthegrowingdigitaldivide
5.1Mitigatingthenegativeeffectsofautomation
5.2Ensuringjobqualityunderaugmentation
5.3Addressingthedigitaldivide
38
38
39
40
X
Conclusion
43
Appendix1.CountrieswithmissingISCO-084-digitdata:estimationprocedure45
References47
AcknowledgementsanduseofGPT51
03ILOWorkingPaper96
ListofFigures
Figure1.Meanautomationscoresbyoccupation,basedonISCOandGPTtasks21
Figure2.TaskswithmediumandhighGPT-exposure,byoccupationalcategory(ISCO1-digit)24
Figure3.Boxplotoftask-levelscoresbyISCO4d,groupedbyISCO1d25
Figure4.Augmentationvsautomationpotentialatoccupationallevel27
Figure5.Occupationswithhighautomationpotential28
Figure6.Occupationswithhighaugmentationpotential29
Figure7a.Automationvsaugmentationpotential:sharesoftotalemployment,microdata
for59countries30
Figure7b.Automationvsaugmentationpotential:sharesoftotalemploymentineachsex
(ILOmicrodata)31
Figure8.CountrycoveragebasedonthelevelofdigitsinISCO-08(ILOdata)33
Figure9a.Globalestimates:jobswithaugmentationandautomationpotentialasshareof
totalemployment34
Figure9b.Automationvsaugmentationpotential:sharesoftotalemploymentforeachsex
(globalestimate)35
Figure10.Occupationswithhighautomationpotential,byISCO4-digitandincomegroup36
Figure11a.The“BigUnknown”:occupationsbetweenaugmentationandautomationpotential37
Figure11b.The“BigUnknown”:shareoftotalemployment,byincomegroup(globalestimate)37
Figure11.Shareofpopulationnotusingtheinternet41
Figure12.Aclassicgrowthpath:incomeandoccupationaldiversification42
04ILOWorkingPaper96
ListofTables
Table1.ISCO-08Structureofoccupationsandtasksusedinthestudy11
Table2.SampleoftasksanddefinitionsfromISCOandpredictedbyGPT-414
Table3.Sampleoftask-levelscores(high-incomecountrycontext)15
Table4.aTestofscoreconsistency(100task-levelpredictions)17
Table4.bTaskswithhighautomationpotentialclusteredintothematic22
groups*
Table5.Groupingofoccupationsbasedontask-levelscores26
Table6.MicrodatacoveragebylevelsISCO-08:numberofcountries32
05ILOWorkingPaper96
Acronyms
3G
ThirdGeneration(referringtoagenerationofstandardsformobiletelecom-munications)
Ada
AlanguagemodelbyOpenAIusedtogenerateembeddings
AGI
ArtificialGeneralIntelligence
AI
ArtificialIntelligence
ANN
ArtificialNeuralNetwork
API
ApplicationProgrammingInterface
ATMs
AutomatedTellerMachines
CPU
CentralProcessingUnit
DL
DeepLearning
DOLE
DepartmentofLaborandEmployment
ESCO
EuropeanSkills,Competences,QualificationsandOccupations
GPTs
GenerativePre-TrainedTransformers
GPT-4
GenerativePre-TrainedTransformer4
GPU
GraphicsProcessingUnit
HIC
High-IncomeCountries
ICT
InformationandCommunicationsTechnology
ILO
InternationalLabourOrganization
ISCO
InternationalStandardClassificationofOccupations
ISCO-08
InternationalStandardClassificationofOccupations2008
K-Means
K-MeansClusteringAlgorithm
LFS
LabourForceSurveys
LIC
Low-IncomeCountries
LLMs
LargeLanguageModels
06ILOWorkingPaper96
LMIC
Lower-Middle-IncomeCountries
ML
MachineLearning
NLP
NaturalLanguageProcessing
OECD
OrganisationforEconomicCo-operationandDevelopment
O*NET
OccupationalInformationNetwork
OpenAI
OpenArtificialIntelligence(organization'sname)
Python
High-levelprogramminglanguage
RL
ReinforcementLearning
SD
StandardDeviation
SMEs
SmallandMedium-sizedEnterprises
UMIC
Upper-Middle-IncomeCountries
US
UnitedStates
USD
UnitedStatesDollar
UMIC
Upper-Middle-IncomeCountries
US
UnitedStates
07ILOWorkingPaper96
XIntroduction
Eachnewwaveoftechnologicalprogressintensifiesdebatesonautomationandjobs.CurrentdebatesonArtificialIntelligence(AI)andjobsrecallthoseoftheearly1900swiththeintroduc-tionofthemovingassemblyline,oreventhoseofthe1950sand1960s,whichfollowedtheintro-ductionoftheearlymainframecomputers.Whiletherehavebeensomenodstothealienationthattechnologycanbringbystandardizingandcontrollingworkprocesses,inmostcases,thedebateshavecentredontwoopposingviewpoints:theoptimists,whoviewnewtechnologyasthemeanstorelieveworkersfromthemostarduoustasks,andthepessimists,whoraisealarmabouttheimminentthreattojobsandtheriskofmassunemployment.
Whathaschangedindebatesontechnologyandworkers,however,isthetypesofworkersaf-fected.Whiletheadvancesintechnologyintheearly,midandevenlate-1900swereprimarilyfocusedonmanualworkers,technologicaldevelopmentsincethe2010s,inparticulartherapidprogressofMachineLearning(ML),hascentredontheabilityofcomputerstoperformnon-rou-tine,cognitivetasks,andbyconsequencepotentiallyaffectwhite-collarorknowledgeworkers.Inaddition,thesetechnologicaladvancementshaveoccurredinthecontextofmuchstrong-erinterconnectednessofeconomiesacrosstheglobe,leadingtoapotentiallylargerexposurethanlocation-based,factory-levelapplications.Yetdespitethesedevelopments,toanaverageworker,eveninthemosthighlydevelopedcountries,thepotentialimplicationsofAIhave,untilrecently,remainedlargelyabstract.
ThelaunchofChatGPTmarkedanimportantadvanceinthepublic’sexposuretoAItools.Inthisnewwaveoftechnologicaltransformation,machinelearningmodelshavestartedtoleavethelabsandbegininteractingwiththepublic,demonstratingtheirstrengthsandweaknessesindailyuse.ThechatfunctiondramaticallyshortenedthedistancebetweenAIandtheenduser,simultaneouslyprovidingaplatformforawiderangeofcustom-madeapplicationsandinno-vations.Giventhesesignificantadvancements,itisnotsurprisingthatconcernsoverpotentialjoblosshaveresurged.
WhileitisimpossibletopredicthowgenerativeAIwillfurtherdevelop,thecurrentcapabilitiesandfuturepotentialofthistechnologyarecentraltodiscussionsofitsimpactonjobs.Scepticstendtobelievethatthesemachinesarenothingmorethan“stochasticparrots”–powerfultextsummarizers,incapableof“learning”andproducingoriginalcontent,withlittlefutureforgen-eralpurposeuseandunsustainablecomputingcosts(Benderetal.2021).Ontheotherhand,morerecenttechnicalliteraturefocusedontestingthelimitsofthelatestmodelssuggestsanincreasingcapabilitytocarryout“novelanddifficulttasksthatspanmathematics,coding,vision,medicine,law,psychologyandmore”,andageneralabilitytoproduceresponsesexhibitingsomeformsofearly“reasoning”(Bubecketal.2023).Someassessmentsgoasfarassuggestingthatmachinelearningmodels,especiallythosebasedonlargeneuralnetworksusedbyGenerativePre-trainedTransformers(GPT,seeTextBox1),mighthavethepotentialtoeventuallybecomeageneral-purposetechnology(Goldfarb,Taska,andTeodoridis2023;Eloundouetal.2023).1Thiswouldhavemultipliereffectsontheeconomyandlabourmarkets,asnewproductsandservic-eswouldlikelyspringfromthistechnologicalplatform.
Associalscientists,wearenotinpositiontotakesidesinthesetechnicaldebates.Instead,wefocusonthealreadydemonstratedcapabilitiesofGPT-4,includingcustom-madechatbotswithretrievalofprivatecontent(suchascollectionsdocuments,e-mailsandothermaterial),natu-rallanguageprocessingfunctionsofcontentextraction,preparationofsummaries,automatedcontentgeneration,semantictextsearchesandbroadersemanticanalysisbasedontextem-beddings.LargeLanguageModels(LLMs)canalsobecombinedwithotherMLmodels,suchas
1Thethreemaincharacteristicsofgeneral-purposetechnologiesarepervasiveness,abilitytocontinueimprovingovertime,andabil-itytospawnfurtherinnovation(JovanovicandRousseau,2005).
08ILOWorkingPaper96
speech-to-textandtext-to-speechgeneration,potentiallyexpandingtheirinteractionwithdif-ferenttypesofhumantasks.Finally,thepotentialofinteractingwithlivewebcontentthroughcustomagentsandplugins,aswellasthemultimodal(notexclusivetotext,butalsocapableofreadingandgeneratingimage)characterofGPT-4makesitlikelythatthistypeoftechnologywillexpandintonewareas,therebyincreasingitsimpactonlabour.
Departingfromtheseobservations,thisstudyseekstoaddtheglobalperspectivetothealreadylivelydebateonpossiblechangesthatmayresultinthelabourmarketsasaconsequenceoftherecentadventofgenerativeAI.Westressthefocusofourworkontheconceptsof“exposure”and“potential”,whichdoesnotimplyautomation,butratherlistsoccupationsandassociatedemploymentfiguresforjobsthataremorelikelytobeaffectedbyGPT-4andsimilartechnologiesinthecomingyears.Theobjectiveofthisexerciseisnottoderiveheadlinefigures,butrathertoanalysethedirectionofpossiblechangesinordertofacilitatethedesignofappropriatepolicyresponses,includingthepossibleconsequencesonjobquality.
Theanalysisisbasedon4-digitoccupationalclassificationsandtheircorrespondingtasksintheISCO-08standard.ItusestheGPT-4modeltoestimateoccupationalandtask-levelscoresofex-posuretoGPTtechnologyandsubsequentlylinksthesescorestoofficialILOstatisticstoderiveglobalemploymentestimates.Wealsoapplyembedding-basedtextanalysisandsemanticclus-teringalgorithmstoprovideabetterunderstandingofthetypesoftasksthathaveahighauto-mationpotentialanddiscusshowtheautomatingandaugmentingeffectswillstronglydependonarangeofadditionalfactorsandspecificcountrycontext.
Wediscusstheresultsofthisanalysisinthebroadercontextoflabourmarkettransformations.Weputparticularfocusonthecurrentdisparitiesindigitalaccessacrosscountriesofdifferentincomelevels,thepotentialforthisnewwaveoftechnologicaltransformationtoaggravatesuchdisparities,andtheensuingconsequencesonproductivityandincome.Wealsogiveconsider-ationtojobswithhighestautomationandaugmentationpotentialanddiscussgender-specificdifferences.Theanalysisdoesnottakeintoaccountthenewjobsthatwillbecreatedtoaccom-panythetechnologicaladvancement.Twentyyearsago,therewerenosocialmediamanagers,thirtyyearsagotherewerefewwebdesigners,andnoamountofdatamodellingwouldhaverenderedaprioripredictionsconcerningavastarrayofotheroccupationsthathaveemergedinthepastdecades.AsdemonstratedbyAutoretal.(2022),some60percentofemploymentin2018intheUnitedStateswasinjobsthatdidnotexistinthe1940s.
Indeed,themainvalueofstudiessuchasthisoneisnotinthepreciseestimates,butratherinunderstandingthepossibledirectionofchange.Suchinsightsarenecessaryforproactivelyde-signingpoliciesthatcansupportorderly,fair,andconsultativetransitions,ratherthandealingwithchangeinareactivemanner.Forthisreason,wealsoemphasizethepotentialeffectsoftechnologicalchangeonworkingconditionsandjobqualityandtheneedforworkplaceconsul-tationandregulationtosupportthecreationofqualityemploymentandtomanagetransitionsinthelabourmarket.
Wehopethatthisresearchwillcontributetoneededpolicydebatesondigitaltransformationintheworldofwork.Whiletheanalysisoutlinespotentialimplicationsfordifferentoccupationalcategories,theoutcomesofthetechnologicaltransitionarenotpre-determined.Itishumansthatarebehindthedecisiontoincorporatesuchtechnologiesanditishumansthatneedtoguidethetransitionprocess.Itisourhopethatthisinformationcansupportthedevelopmentofpoliciesneededtomanagethesechangesforthebenefitofcurrentandfuturesocieties.Weintendtousethisbroadglobalstudyasanopeningtomorein-depthanalysesatcountrylevel,withaparticularfocusondevelopingcountries.
09ILOWorkingPaper96
XTextBox1:WhatareGPTs?
GenerativePre-TrainedTransformersbelongtothefamilyofLargeLanguageModels–atypeofMachineLearningmod-elbasedonneuralnetworks.The“generative”partreferstotheirabilitytoproduceoutputofacreativenature,whichinlanguagemodelscantaketheformofsentences,paragraphs,orentiretextstructures,withcharacteristicsoftenun-distinguishablefromthatproducedbyhumans.“Pre-trained”referstotheinitialtrainingonalargecorpusoftextdata,typicallythroughunsupervisedorself-supervisedlearning,duringwhichthemodellearnsaboutthetextstructurebytemporarilymaskingpartofthecontentandtryingtominimizeerrorsinthepredictionofthemaskedwords.Followingpre-training,suchmodelsarefurtherfine-tunedwiththeuseoflabelleddataandso-called“reinforcementlearning”,makingthemmoresuitableforspecifictasks.Thispartoftrainingisoftenperceivedasaspecializedjob,executedbyahandfuloftechnicalexperts.Inreality,itislabourintensiveandinvolvesmanyinvisiblecontributors(Dzieza2023).Itsprerequisiteistheproductionofvastamountsoflabelleddata,typicallydonebyworkersoncrowdsourcingplatforms.“Transformers”refertotheunderlyingmodelarchitecture,whichusesnumerousmechanisms,suchasattentionandself-attentionframeworks,todevelopweightsrelatedtotheimportanceoftextelements,suchaswordsinasentence,whicharesubsequentlyusedforpredictions(Vaswanietal.2017).
WhileGPTspecificallyreferstomodelsdevelopedbyOpenAI(GPT-1,2,3and4),thistypeofarchitectureisusedbymanymorelanguagemodelsalreadyavailablecommercially.ThelaunchofChatGPTon30November2022madeGPTsmorepopularamongthepublic,asitmadeitpossibleforindividualswithnoprogrammingknowledgetointeractwithGPT-3(andeventuallyGPT-4)throughachatbotfunctionwithahuman-liketone.Forresearchpurposesandmorecom-plexapplications,suchlanguagemodelsaretypicallymorepowerfulwhenusedthroughanApplicationProgrammingInterface(API).AnAPIisadeveloperaccesspointthatreliesonaquery-responseprotocolwiththeuseofprogrammingsoftware.Inourcase,werelyonaPythonscriptbasedonOpenAIlibrary,designedtoconnecttoGPT-4model,provideafine-tunedpromptandreceivearesponse,whichissubsequentlystoredinadatabaseonourserver.ThisenablesbulkprocessingoflargenumbersofrequestsandreliesontheGPT-4modelwithmoreparametersthanwhatisaccessiblethroughthepublicChatfunction.
10ILOWorkingPaper96
X1MethodsandData
Therearetwoprincipalapproachestotheanalysisofautomationofoccupations(GeorgieffandHyee2021).Thefirstistousedataonjobvacanciestounderstandhowdemandforspecificskillsevolvesovertime.Moststudiesusingthisapproachharnessdatafromonlinerecruitmentplat-forms(CammeraatandSquicciarini2021;Acemogluetal.2022)tomeasurethefrequencyofref-erencestoAI(ortoanyothertechnologyofinterest)inthetextofthejobdescription.Theseref-erencesarethenusedasaproxyforthedemandforspecificskillsand,byitsextension,aproxyfortherateoftechnologicaladoptionattheenterpriselevel.Thisapproachworkswellincoun-trieswithahighonlinepresenceinrecruitment,thoughitdoesnotalwayscapturetheindus-triesaffectedasaresultofsubcontracting.Theapproach,however,islesswellsuitedforaglobalstudycoveringcountrieswithlessonlinepresence,asmostvacanciesarenotadvertisedonon-lineplatformsbutrecruitedthroughothermeansofcommunication(GeorgieffandHyee2021).
Thesecondapproachistofocusonoccupationalstructures,withtheideaofestimatingtheau-tomationpotentialoftasksorskillsthatmakeupagivenjob.Theadvantageofthismethodisthatsuchoccupationalclassificationscaneasilybelinkedtoofficiallabourmarketstatistics,whichisofparticularimportanceforunderstandingglobal,regionalandincome-baseddiffer-entials.Thisstrandofliteratureisrich,butfrequentlymisunderstood,especiallywhenitcomestocommunicatingitsfindingstothepublic,asmediainterpretationstendtoblurthedistinc-tionbetweenautomationpotentialandactualdeploymentintheworkplace.Forexample,FreyandOsborne’s(2013,2017)influentialstudyhasbeencitedover12,000times,oftenfordiffer-enttypesofdoomsdaypronouncements,eventhoughtheauthorswereclearaboutthedistinc-tionbetweenpotentialandpredictedeffects.Arangeofstudiesfollowthisresearchtradition,attemptingtocalculatedifferenttypesofoccupationalautomationscoresinOECDcountries(Brynjolfsson,Mitchell,andRock2018;Felten,Raj,andSeamans2018;Felten,Raj,andSeamans2019;AcemogluandRestrepo2020;FossenandSorgner2022)orevencombiningoccupationalandjobpostingdata(GeorgieffandHyee2021).Someauthorshavealsotakenupthechallengeofproducingbetterestimatesfordevelopingcountries(BalliesterandElsheikhi2018),oftenbytryingtolinkdetailedoccupationaldataand
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