关注AI鸿沟：塑造未来劳动世界的全球视角（英）-24正式版-WN8

MindtheAIDivideShapingaGlobalPerspectiveontheFutureofWorkMindtheAIDivide:ShapingaGlobalPerspectiveontheFutureofWorkCopyright©2024UnitedNationsAllrightsreservedworldwide.Nopartofthispublicationmay,forcommercialpurposes,bereproducedortransmittedinanyformorbyanymeans,electronicormechanical,includingphotocopy,recordingoranyinformationstorageandretrievalsystemnowknownortobeinvented,withoutwrittenpermissionbythepublisher.RequeststoreproduceexcerptsortophotocopyshouldbeaddressedtotheCopyrightClearanceCenterat.Allotherqueriesonrightsandlicenses,includingsubsidiaryrights,shouldbeaddressedto:UnitedNationsPublications,405East42ndStreet,S-11FW001,NewYork,NY10017,UnitedStatesofAmerica.Email:permissions@.Website:.ThedesignationsemployedandthepresentationofthematerialinthispublicationdonotimplytheexpressionofanyopinionwhatsoeveronthepartoftheSecretariatoftheUnitedNationsconcerningthelegalstatusofanycountry.PDFISBN:9789211066524ForewordTheunevenadoptionofArtificialIntelligence(AI)isacriticalissuethatgoesbeyondeconomicgrowth.Itimpactsglobalequity,fairnessandthesocialcontractthatisattheheartofsocialjustice.Disparitiesinaccesstorobustinfrastructure,advancedtechnology,qualityeducationandtrainingaredeepeningexistinginequalities.AstheglobaleconomyincreasinglyshiftstowardsAI-drivenproductionandinnovation,lessdevelopedcountriesriskbeingleftfurtherbehind,exacerbatingeconomicandsocialdivides.Withouttargetedandconcertedeffortstobridgethisdigitaldivide,AI’spotentialtofostersustainabledevelopmentandalleviatepovertywillremainunrealized,leavingsignificantportionsoftheglobalpopulationdisadvantagedintherapidlyevolvingtechnologicallandscape.DuringtheconsultationsheldbytheSecretary-General’sHigh-levelAdvisoryBodyonArtificialIntelligence,ithasbecomeclearthattheworldofworkisattheheartoftheadoptionofAI.ItisthuscriticaltounderstandthepotentialforAItoaffectlabourdemandandtransformoccupations.Itisattheworkplacewherethepotentialforproductivitygainsandimprovedworkingconditionsforthebenefitofworkers,theirfamilies,andsocietiesatlarge,canberealized.Butsuchbenefitswillnothappenontheirown;theywillonlybeachievediftherightconditionsareinplace,includingtheavailabilityofdigitalinfrastructureandskills,butalsoacultureofsocialdialoguethatfostersapositiveintegrationoftechnology.PromotinginclusivegrowthrequiresproactivestrategiestosupportAIdevelopmentincountriesonthewrongsideoftheAIdivide.Thisinvolvesenhancingdigitalinfrastructure,promotingtechnologytransfer,buildingAIskills,andensuringthatalljobsalongtheAIvaluechainareofgoodqualityandimprovethelivesofworkingpeople.ByprioritizinginternationalcollaborationinAIcapacitybuilding,wecancreateamoreequitableandresilientAIecosystem,unlockingopportunitiesforsharedprosperityandhumanadvancementworldwide.WelookforwardtocontinuingourcollaborativeeffortstoshapetheglobalgovernanceofAI,upholdhumandignityandlaborstandards,andexpandeconomicopportunityforall.AmandeepSinghGillGilbertF.HoungboUnitedNationsSecretary-General’sEnvoyonTechnologyDirector-GeneraloftheInternationalLabourOrganizationMindtheAIDivide:ShapingaGlobalPerspectiveontheFutureofWork|3ContentsForeword3Section1.Introduction5Section2.Unevenground:UnderstandingAI’sroleinreshapinglabourmarketsEnsuringjobqualityunderaugmentationSection3.TheAIvaluechainandthedemandforskillsAdaptingskillsfortheAIlandscape610111417Section4.Movingforward:Strengtheninginternationalcooperation,buildingnationalcapacity,andaddressingAIintheworldofworkStrengthenedinternationalcooperationonAIBuildingnationalAIcapacity1718182021TowardsapositiveintegrationofAIintheworldofworkAcknowledgmentsReferences4|MindtheAIDivide:ShapingaGlobalPerspectiveontheFutureofWorkSection1IntroductionTherapidadvancementofArtificialIntelligence(AI)promiseswidespreadtransformationsforoursocieties,oureconomiesandtheworldofwork.Whilesuchadvancesoffertremendousopportunitiesforinnovationandproductivity,theunevenratesofinvestment,adoptionanduseamongcountriesrisksexacerbatingthealreadywidedisparitiesinincomeandqualityoflife.Thereisapronounced“AIdivide”emerging,wherehighincomenationsdisproportionatelybenefitfromAIadvancements,whilelow-andmedium-incomecountries,particularlyinAfrica,lagbehind.Worse,thisdividewillgrowunlessconcertedactionistakentofosterinternationalcooperationinsupportofdevelopingcountries.Theabsenceofsuchpolicieswillnotonlywidenglobalinequalities,butriskssquanderingthepotentialofAItoserveasacatalystforwidespreadsocialandeconomicprogress.dialogue.Socialdialogueonthedesign,implementationanduseoftechnologyattheworkplace,aswellasinthedevelopmentofregulationsessentialforensuringrespectofworkers’fundamentalrights,isneeded.Indeed,whethertheintegrationoftechnologyintoworkprocessesspursproductivitygrowthorimprovesworkingconditionsinsupportofsocialjusticedependsinlargepartonthestrengthofsuchcollaborationanddialogue.SovereigneffortsplayacrucialroleinshapingAIcapacitybuildingascountriesasserttheirautonomyindevelopingstrategiesandpoliciestailoredtotheiruniquesocio-economiccontexts.Localprocesses,drivenbyculturalvalues,politicaleconomies,andsocietalneeds,cansignificantlyimpacttheeffectivenessandsustainabilityofAIinitiatives.However,disparitiesinresourcesandexpertiseremainandcanhinderAIdevelopmentintheGlobalSouth.Inresponse,thereisagrowingrecognitionoftheresponsibilityofdevelopedcountriestosupportcapacitybuildingeffortsinresourcescarcecountries.AsoutlinedintherecentInterimReportoftheUnitedNationsSecretary-General’sHigh-levelAdvisoryBodyonAI1,thisrecognitionextendsbeyondfinancialaidtoincludeknowledgesharing,skillsdevelopment,technologytransfer,aswellascollaborativeresearchanddevelopmentpartnerships.ByleveragingtheiradvancedAIecosystems,theGlobalNorthcanhelpbridgethegapandempowercountriesintheGlobalSouthtoharnessAIforsustainabledevelopment,whilerespectingtheirsovereigntyandpromotinglocalinnovationecosystems.ByprioritizingglobalcollaborationforAIcapacitybuilding,theinternationalcommunitycannurtureamoreequitableandresilientglobalAIecosystem,unlockingopportunitiesforsharedprosperityandhumanflourishingacrosstheworld.WhileAIwillpotentiallyaffectmanyaspectsofourdailylives,itsimpactislikelytobemostacuteintheworkplace.WealthiercountriesaremoreexposedtothepotentialautomatingeffectsofAIintheworldofwork,buttheyarealsobetterpositionedtorealizetheproductivitygainsitoffers.Developingcountries,ontheotherhand,maybetemporarilybufferedbecauseofalackofdigitalinfrastructure,butthisbufferrisksturningintoabottleneckforproductivitygrowth,andmoreimportantly,forthefutureprosperityoftheirpopulations.EnsuringinclusivegrowthinthefuturerequiresproactivemeasurestoempowerAIdevelopmentincountriesatthedisadvantagedreceivingendofthedigitaldivide,fosteringdigitalinfrastructureaswellasAIskills,andpromotingtechnologytransferandabsorption.SuchdigitalskillscanalsoenableamorepositiveintegrationofAIintheworkplace,particularlywhencombinedwithsocial1/ai-advisory-bodyMindtheAIDivide:ShapingaGlobalPerspectiveontheFutureofWork|5Section2UnevengroundUnderstandingAI’sroleinreshapinglabourmarketsResearchonthepossibleeffectsofgenerativeAIonemploymentacrosstheworldsuggeststhatwhiletherearelikelytobeimportanttransformativeeffectsonsomeoccupations,impactsintermsofjoblossesaremuchlessthanheadlinefiguresappearinginthemedia,andcertainlydonotpointtoajoblessfuture.AccordingtoananalysisundertakenbytheInternationalLabourOrganizationonthepotentialexposureoftaskstogenerativeAItechnology,clericalsupportworkersarethemostexposedoccupationalgroupwith24percentofthetasksinthesejobsassociatedwithhighlevelofexposuretoautomationandanother58percentwithmedium-levelexposure(seeFigure1).2Otheroccupationalgroupsarelessexposed,withonly1to4percentoftasksconsideredashavinghighautomationpotential,andmedium-exposedtasksnotexceeding25percent.Thismeansthat,whilecertaintasksintheseoccupationscouldpotentiallybeautomated,mosttasksstillrequirehumanintervention.Suchpartialautomationcouldenableefficiencygains,byallowinghumanstospendmoretimeonotherareasofwork.Importantly,taskautomationdoesnotnecessarilyimplyredundancies,asthetechnologycanalsocomplementoraugmenthumanlabourwhenonlycertaintasksareautomated.Whethertheadoptionofthetechnologyleadstoautomation(jobloss)oraugmentation(jobcomplementarity)dependsonthecentralityoftheautomatedtasktotheoccupation,howthetechnologyisintegratedFigure1:Taskswithmediumandhigh-levelexposuretogenerativeAItechnologybymajoroccupationalgroup(ISCO1-digit)Source:Gmyreketal.,2023.2Thestudyanalysesthepotentialforautomationwiththe436internationallystandardizedISCO-08

occupationsandthenclassifiestheoccupationbasedonthemeanandstandarddeviationofthescore.Formoredetailssee[1].6|MindtheAIDivide:ShapingaGlobalPerspectiveontheFutureofWorkintoworkprocessesandmanagement’sdesiretoretainhumanstoperformoroverseesomeofthetasks,despitethepotentialofautomation.AItechnologyismuchhigher,duetotheirover-representationinclericaloccupations(seefigure2).Inmostregions,thepotentialexposureofwomenismorethandoublethatofmen’sexposure.Someofthisemploymentisinbusinessprocessoutsourcing,suchascontactorcallcenterwork,whichisanimportantpartoftheeconomyofseveraldevelopingcountries,includingIndiaandthePhilippines.Theindustryisanimportantsourceofformalandrelativelywell-paidemployment,particularlyforwomen.Whilepotentialexposuredoesnotnecessarilytranslatetodisplacement,itisclearthattheadvancesintechnologymayputsomeofthesejobsatrisk.3TheILOanalysisusesoccupationalexposurescores(themeanexposureofeachofthetaskswithinanoccupation)andappliesthesescorestoemploymentdatafromlabourforcesurveysofmorethan140countriestoassesspotentialemploymentimpactattheglobalandregionallevel.Withrespecttoautomation,theshareofemploymentthatisexposedishighestinEuropeandNorthernAmerica,reflectingthegreatereconomicandlabourmarketdiversificationoftheseregions.InLatinAmerica,AsiaandAfrica,theshareofemploymentpotentialexposedtoautomationismuchsmaller,duetothegreatershareofworkersemployedinoccupationsthatwouldnotbeexposedtogenerativeAItechnologysuchasinagriculture,transportorfoodvending.Anotherfindingisthatasignificantlylargershareoftotalemploymentisinoccupationswithhighaugmentationpotential,andthisholdsacrossregions,from10.2percentinSub-SaharanAfricato16.1percentinSoutheasternAsiaandthePacific(Seefigure3).Thus,thepotentialforoccupationstobenefitfromtheproductivity-enhancingeffectsofthetechnologyisrelativelysimilaracrosscountries.Inpractice,however,itislesslikelyNevertheless,women’spotentialexposuretotheautomatingeffectsofgenerativeFigure2:Potentialexposuretoautomationbyglobalsub-region3‘AICouldKilloffMostCallCentres,SaysTataConsultancyServicesHead’,April25,2024.MindtheAIDivide:ShapingaGlobalPerspectiveontheFutureofWork|7Figure3:Potentialexposuretoaugmentationbyglobalsub-regiontoberealizedduetoconstraintsinphysicalinfrastructure(electricityaccess,broadband)aswellasdigitalskills.Indeed,subsequentresearchthatincorporatesdataoncomputeruseatwork[2]revealsthatmanyoftheoccupationswithpotentialforaugmentationhaverelativelylowusageofcomputeratwork,suggestingthattheconditionsarenotinplaceforrealizingthepotentialproductivitygains.AscanbeseeninFigure4,theshareofworkerswithoutaccesstoacomputeratwork(“nocomputer”)exceedsthosewhouseacomputerin9ofthe16countrieslisted.AsFigure4:PotentialexposuretoaugmentationandcomputeruseatworkSource:Gmyrek,WinklerandGarganta,2024.8|MindtheAIDivide:ShapingaGlobalPerspectiveontheFutureofWorksuch,thelikelihoodtorealizeproductivitygainsfromgenerativeAItechnologywillbelimited.ofgenerativeAItechnologyintheircallcentrework,theirdigitalinfrastructureandskilledworkforcecanalsobeanassetforspawningthegrowthofcomplementaryindustries.Harnessingsuchpotentialisparamount.Figure5givesinformationonthecharacteristicsofthosewhomightbeaffectedbyautomationfromgenerativeAItechnologyinLatinAmerica.Asthedatashow,itiseducatedwomenlivinginurbanareasandbelongingtothetopfifthoftheincomedistributionthataremostexposed.ForLatinAmerica,theseoccupationsareoverwhelminglyinsalaried,formalemploymentandinthesectorsoffinance,professionalservicesandpublicadministration.Inshort,theyaregoodjobs,whoselosswouldhavenegativemultipliereffectsbotheconomicallyandsocially.Indeed,withtherightconditionsinplace,anewwaveoftechnologycouldfuelgrowthopportunities.Inthepast,technologicaladvancementshavespurrednewandsuccessfulindustriesinmanydevelopingcountries.OnesuchexampleistheM-Pesamoneyservice,whichreliedonthediffusionofmobiletelephonesinKenya.Theservice,inturn,increasedfinancialinclusionwhichhelpedtopropelthegrowthofSMEsandledtocreationofanetworkof110,000agents,40timesthenumberofbankATMsinKenya[3];[4].Similarly,astudyofthediffusionof3GcoverageinRwandabetween2002and2019foundthatincreasedmobileinternetcoverageTheanalysisdoesnotaddressthepotentialfornewjobcreation.Thus,whilemiddle-incomecountriessuchasIndiaandthePhilippines,aremoreexposedtotheautomatingeffectsFigure5:Characteristicsofpersonsholdingoccupationsmostexposedtoautomation,LatinAmericaSource:Gmyrek,WinklerandGarganta,2024(forthcoming).MindtheAIDivide:ShapingaGlobalPerspectiveontheFutureofWork|9waspositivelyassociatedwithemploymentgrowth,increasingbothskilledandunskilledoccupations[5].Scholars[6]alsofindpositiveemploymenteffects,fromthearrivalofinternetin12Africancountries,albeitwithaslightbiastowardsskilledoccupations.Thesegainsareattributedtoincreasesinproductivityandgrowthofmarketsthatfollowedincreasedconnectivity,underliningtheneedforsuchinvestments,givenimportantmultipliereffectsontheeconomyandlabourmarkets.Asaresult,whethertheeffectoftechnologyonworkingconditionsispositiveornegativedependsinlargepartonthevoicethatworkershaveinthedesign,implementationanduseoftechnology.Havingsuchagencyreliesinturnontheopportunitiesforworkerparticipationanddialogue.Thiscantakeplaceeitherthroughformalizedsettings,suchasworkscouncilsorguidanceprovidedincollectivebargainingagreements,orlessformally,inworkplaceswherethereisahighdegreeofemployeeengagement.StudiesinEuropehaveshownthatitiscountrieswithstrongerandmorecooperativeformsofworkplaceconsultation,essentiallytheNordiccountriesandGermany,whereworkersaremoreopentotechnologicaladoptionattheworkplace[10].EnsuringjobqualityunderaugmentationAnotherareaofconcernisabouttheimpactofAItechnologyonworkingconditionsandjobqualitywhenthetechnologyisintegratedintotheworkplace.Whilesuchintegrationintoworktaskscanpotentiallypromotemoreengagingworkifroutinetasksareautomated,itcanalsobeimplementedinwaysthatlimitsworkers’agencyoracceleratesworkintensity.ConcernsoverAI’sintegrationattheworkplacehasfocusedonthegrowthofalgorithmicmanagement,essentiallyworksettingsinwhich“humanjobsareassigned,optimized,andevaluatedthroughalgorithmsandtrackeddata”[7].Algorithmicmanagementisadefiningfeatureofdigitallabourplatforms,butitisalsopervasiveinofflineindustriessuchasthewarehousingandlogisticssectors.Inwarehousesanautomated,“voice-picking”systemdirectswarehousestafftopickcertainproductsinthewarehouse,whileusingdatacollectiontomonitorworkersandsetthepaceofwork[8].Besideslackingautonomytoorganizetheirworkorsetitspace,workersalsohavelittleabilitytoprovidefeedbackordiscusswithmanagementabouttheorganizationofwork[9].TheintegrationofgenerativeAIintootherfieldssuchasbanking,insurance,socialservices,andcustomerservicemorebroadlymayhaveasimilareffect.Technologicaladvancementsareoftenfeltmoreimmediatelyattheworkplacelevelandareusuallybestaddressedattheworkplace.10|MindtheAIDivide:ShapingaGlobalPerspectiveontheFutureofWorkSection3TheAIvaluechainandthedemandforskillsLiketheproductionofmanygoodsandWhendataiscollected,itisusuallyservicesintheglobaleconomy,AIhasitsownvaluechain.AsdepictedinFigure6,therearedifferentstagesoftheAIvaluechain,eachwithspecifichumanandsocialinfrastructureneeds.Asistypicalinmostglobalvaluechains,stagesdifferintheamountofvaluereceivedforthecontributionmade,withlower-valueaddedactivitiespredominantinmiddleandlow-incomecountriesanddesignanddeploymentassociatedwithhigher-incomecountries.unstructured.Highlyskilleddataengineerswillpre-processthedataintoausableformat,but‘datalabelers’areneededtolabelandclassifydatasothatitisusable.Labelledandannotateddatasetsarecriticalforthedevelopmentandeffectivenessofmachinelearningmodels.WorkersinvolvedindataenrichmentcarryoutanarrayoftasksthatincludemarkingradiologyscanstoaidincreatingAIsystemscapableofdetectingcancer;categorizingtoxicandunsuitableonlinecontenttoimprovecontentmoderationalgorithmsordiminishthenegativityinlargelanguagemodelresponses;annotatingvideofootagefromdrivingsessionstotrainautonomousvehicles;editinglargelanguagemodeloutputstoboosttheirfunctionality;andmore.4DataisfundamentaltothedevelopmentandoperationofAIsystems.Human-prepareddataisfedintoAIsystemstohelpthemlearnthenecessaryconnectionsandpatternsforfunctionality.Thesourcesofthisdataarediverse,dependingonthesystem’spurpose.Publiclyavailabledata,suchasUnitedNationsdocumentsusedfortrainingtranslationprograms,contributedtoadvancesinnaturallanguageprocessing.Proprietarydataisalsocrucial,particularlyinworkplaceapplications,likecallcenterrecordingsusedtotrainchatbotsforcustomerservice.Withglobalconnectivity,datacollectioncontinuestoprovidetheessentialrawmaterialforfutureAIapplications.Contentmoderationistheprocessofmonitoringandfilteringuser-generatedcontentondigitalplatforms,suchassocialmedia,forums,andwebsites,toensurethatitcomplieswiththeplatform’sguidelinesandpolicies.Thegoalofcontentmoderationistomaintainasafe,respectful,andpositiveenvironmentforallusersbyremovingorFigure6:ValuechainofAI1234567Note:Orangerepresentstheactivitiesthathavelowervalue-added.Source:Authors’elaboration.4ValuingDataEnrichmentWorkers:TheCaseforaHuman-CentricApproachtoAIDevelopment|UnitedNationsMindtheAIDivide:ShapingaGlobalPerspectiveontheFutureofWork|11flaggingcontentthatisinappropriate,offensive,harmful,orillegal.Contentmoderationcanbeperformedmanuallybyhumanmoderatorsorautomaticallybyusingalgorithmsandmachinelearningtools.Thetypesofcontentthatmaybesubjecttomoderationcanvarywidely,includingbutnotlimitedtohatespeech,harassment,violence,nudity,andfalseinformation.Evenwiththeuseofalgorithmsandmachinelearningtoolsforcontentmoderation,thereistypicallyalwaysahumaninvolvedintheprocess.Thesetechnologiescanhelpautomateandscalethemoderationprocess,buttheyarenotperfectandcansometimesmakemistakesormissnuancesthatahumanmoderatorwouldbeabletopickupon.afewsimplelinesofcodewhenworkingonanalgorithm[11].InadditiontoplatformssuchasAMTandAppen,datalabelerssometimesworkthroughthird-partycompanieshiredbyleadingtechfirms,inasubcontractingrelationship.AlthoughtherearestillmanydatalabelersworkingintheUnitedStatesinEurope,muchoftheworkisbeingdoneindevelopingcountries,giventhelowremunerationassociatedwiththework.Whileprecisefiguresonthenumbersofpersonsworkingasdatalabelersdonotexist,estimatesrangeinthetensofmillions,anddemandforsuchworkislikelytocontinueasAIdatasetsandtrainingneedsgrow[12].ThesizeofthemarketisestimatedatbetweenUS$1-$3billionandlikelytoexperiencedouble-digitgrowthoverthenext5years[13].Inmanycases,algorithmsareusedtoflagorprioritizecontentforreviewbyhumanmoderators,whothenmakethefinaldecisiononwhetherthecontentshouldberemovedorallowedtoremainontheplatform.Additionally,humanmoderatorsmayalsobeinvolvedintrainingandimprovingthealgorithms,byprovidingfeedbackandlabellingdatathatcanbeusedtorefinethesystem’saccuracyandeffectiveness.Individualstaskedwithcontentmoderationdutiesinsocialmediaplatformsoftensufferfromanxiety,depression,andpost-traumaticstressdisorder,adirectconsequenceoftheircontinuousexposuretodistressingmaterialssuchasmurder,suicide,sexualassault,orchildabusevideos.Datalabelingworkdoesnotrequiremanyqualifications,besidesliteracy,digitalskillsandaccesstocomputer(ormobiledevice)andinternet.StudiesofearningsofonlineplatformworkersintheUSthatperformthiswork,regularlyreportmedianearningsofroughly$2-$3perhour,orwellbelowthefederalminimumwageofUS$7.25[14];[11].Giventhelowlevelofpay,itisunsurprisingthatmuchofthisworkhasmovedtodevelopingcountries.Butevenfromadevelopingcountryperspective,theearningsarelow,particularlyconsideringtheskillleveloftheworkforce,withmanyworkersholdinguniversityandpost-graduatedegrees[11].Fortheworkerswhoworkthroughdigitallabourplatforms–andnotbusinessprocessoutsourcingfirms–thereistheaddedconcernthattheyarehiredasindependentcontractorsandarethusnotcoveredbytheprotectionsandbenefitsemanatingfromastandardemploymentrelationship.Moreover,analysesofearningsdifferentialsbetweenworkersinIndiadoingsimilartypesofdataannotationworkrevealedthatplatformworkersearnedtwo-thirdslessthancomparable,non-platformworkeremployees,evenbeforeaccountingforotherbenefitssuchassocialinsurancecontributions[15].Theseexamplesdemonstratehowhumansareintegraltotheprovisionofservicesmarketedordescribedas“artificialintelligence”.Indeed,JeffBezosdescribedAmazon’sMechanicalTurk(AMT)platformas“artificial-artificial-intelligence”asitwashumanintelligencethatwasprovidingthelabour-intensiveworkneededforartificialintelligencesystemstooperate.AsdescribedontheAMTsite,theplatformprovides“anon-demand,scalable,humanworkforcetocompletejobsthathumanscandobetterthancomputers,forexample,recognizingobjectsinphotos”.5Workersontheplatformareaccessiblethroughanapplicationprogramminginterface(API),allowingprogrammerstocallonworkerswith5SeeAmazonMechanicalTurkAPIReference-AmazonMechanicalTurk.Accessedon9June2024.12

|MindtheAIDivide:ShapingaGlobalPerspectiveontheFutureofWorkButevenamongbusinessprocessoutsourcingfirms,thereareconcernsabouttheworkingconditionsoftheseworkers,withonecasestudyofadataannotationenterprisewithofficesinKenyarevealinglowpay,insecureworkandgender-basedworkplaceviolence[16].Furthermore,thestudyarguedthatthedataannotationskillsusedinthislineofworkwerenotessentiallytransferable,questioningthecareer-enhancingimpactofthislineofwork.havefewerthan20top-tierdatacentres.Thedisparityindatacentreconstructionisunambiguous,withtheUShavingbuilt19timesmoreleadingcloudandco-locationdatacentresthanIndia,whichhasthemostdatacentresamongemerging-marketeconomies.8TheAIdivideisstark–anditispreciselyatthisstagethatpolicyattentionisneededtosupportinvestmentsbothinphysicalinfrastructure(computingpoweror“compute”)andskills.Andsuchinvestmentsareexpensive,puttingdevelopingcountriesandtheirhome-grownstart-upsataseveredisadvantage.Forexample,OpenAIspentapproximately$78millionofcomputetotrainGPT-4,whileGoogle’sGeminiUltra’scomputecostswereestimatedat$191million[17].Movingalongthevaluechain,thesubsequentparts–modeldesign,modeltrainingandtuning,deploymentandmaintenance–representacontrastingpicturewiththeskillsneedsandworkingconditionsofdataannotationwork.Theyalsoinvolvemuchgreaterrequirementsforphysicalinfrastructure,particularlycomputepowernecessaryformodeltrainingandtuning.ThesestagesrequiretheskillsofhighlyqualifiedcomputerscientistsorgraduatesfromotherSTEM6fieldsinadditiontosignificantinvestmentsinresearchanddevelopment.Moreover,thereareknock-oneffectsfrompre-existingmarketpositions.Leadershipintheappmarketisimportantasappsgenerateadditionaluserdatathatisthenusedtoexpandthedatabaseonwhichmachinelearningalgorithmstrainandimprove.Asia,EuropeandNorthAmericahavealmoste