2024年人工智能治理實踐報告

AIGovernanceinPracticeReport2024

Tableofcontents

What'sinside?

Executivesummary 3

PartI.UnderstandingAIandgovernance 6

PartII.Thedatachallenge 15

PartIII.Theprivacyanddataprotectionchallenge 23

PartIV.Thetransparency,explainability

andinterpretabilitychallenge 32

PartV.Thebias,discriminationandfairnesschallenge 41

PartVI.Thesecurityandrobustnesschallenge 50

PartVII.AIsafety 55

PartVIII.Thecopyrightchallenge 61

PartIX.Third-partyAIassurance 65

Conclusion 69

Contacts 70

AIGovernanceinPracticeReport2024|2

Executive

summary

Recentandrapidlyadvancing

breakthroughsinmachine

learningtechnologyhaveforevertransformedthelandscapeofAI.

AIsystemshavebecomepowerfulenginescapableof

autonomouslearningacrossvastswathsofinformationand

generatingentirelynewdata.Asaresult,societyisinthemidstofsignificantdisruptionwiththesurgeinAIsophisticationandtheemergenceofaneweraoftechnologicalinnovation.

AsbusinessesgrapplewithafutureinwhichtheboundariesofAIonlycontinuetoexpand,theirleadersfacetheresponsibilityofmanagingthevariousrisksandharmsofAI,soitsbenefitscanberealizedinasafeandresponsiblemanner.

Critically,thesebenefitsareaccompaniedbyserious

considerationsandconcernsaboutthesafetyofthistechnologyandthepotentialforittodisrupttheworldandnegatively

impactindividualswhenleftunchecked.Confusionabouthowthetechnologyworks,theintroductionandproliferationofbiasinalgorithms,disseminationofmisinformation,andprivacy

rightsviolationsrepresentonlyasliverofthepotentialrisks.

Thepracticeof

AIgovernance

isdesignedtotacklethese

issues.Itencompassesthegrowingcombinationofprinciples,laws,policies,processes,standards,frameworks,industry

bestpracticesandothertoolsincorporatedacrossthedesign,development,deploymentanduseofAI.

AIGovernanceinPracticeReport2024|3

→Executivesummary

TABLEOFCONTENTS↑

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GlobalAIprivateinvestment

(USDbillion,2021)

94

47

42

38

22

18

10

8

4

201320142015201620172018201920202021

Whilerelativelynew,thefieldofAIgovernanceismaturing,withgovernmentauthorities

aroundtheworldbeginningtodeveloptargetedregulatoryrequirementsandgovernance

expertssupportingthecreationofacceptedprinciples,suchastheOrganisationfor

EconomicCo-OperationandDevelopment's

AI

Principles

,emergingbestpracticesandtoolsforvarioususesofAIindifferentdomains.

Therearemanychallengesandpotential

solutionsforAIgovernance,eachwithuniqueproximityandsignificancebasedonan

organization'srole,footprint,broaderrisk-governanceprofileandmaturity.Thisreportaimstoinformthegrowing,increasingly

empoweredandincreasinglyimportant

communityofAIgovernanceprofessionalsaboutthemostcommonandsignificant

challengestobeawareofwhenbuilding

andmaturinganAIgovernanceprogram.

Itoffersactionable,real-worldinsights

intoapplicablelawandpolicy,avarietyofgovernanceapproaches,andtoolsusedto

managerisk.Indeed,someofthechallengestoAIgovernanceoverlapandrunthrough

arangeofthemes.Therefore,anemergingsolutionforonethematicchallengemay

alsobeleveragedforanother.Conversely,in

certaincircumstances,specificchallengesandassociatedsolutionsmayconflictandrequire

reconciliation

withotherapproaches.Someofthesepotentialoverlapsandconflictshavebeenidentifiedthroughoutthereport.

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Questionsaboutwhetherandwhen

organizationsshouldprioritize

AIgovernancearebeinganswered:

"yes"and"now,"respectively.

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Questionsaboutwhetherandwhenorganizationsshouldprioritize

AIgovernancearebeinganswered:"yes"and"

now

,"respectively.

Thisreportis,therefore,focusedonhoworganizationscanapproach,buildandleverageAIgovernanceinthecontextoftheincreasingly

voluminousandcomplexapplicablelandscape.

UzmaChaudhry

IAPPAIGovernance

CenterResearchFellow

AshleyCasovan

IAPPAIGovernanceCenterManagingDirector

JoeJones

IAPPDirectorofResearchandInsights

LuisaResmerita

FTITechnologySeniorDirector

NinaBryant

FTITechnologySeniorManagingDirector

MichaelSpadea

FTITechnologySeniorManagingDirector

AIGovernanceinPracticeReport2024|5

PartI.

UnderstandingAIand

governance

ComponentsofanAIsystemandtheirgovernance

TounderstandhowtogovernanAIsystem,itisimportanttofirst

understandwhatanAIsystemis.TheEUAIAct,forexample,

definesanAIsystemas"amachine-basedsystemthatisdesigned

tooperatewithvaryinglevelsofautonomyandthatmayexhibit

adaptivenessafterdeployment,andthat,forexplicitorimplicit

objectives,infers,fromtheinputitreceives,howtogenerateoutputssuchaspredictions,content,recommendations,ordecisionsthat

caninfluencephysicalorvirtualenvironments."

AsindicatedintheOECD'sFrameworkfortheClassificationofAI

systems,AIsystemsarecomprisedofdatausedtotrainandoperateasystem,model,outputandcontext.WhileamodelisafundamentalbuildingblockofanAIsystem,asinglemodelseldomoperatesin

isolation.Instead,multipleAImodelscometogetherandinteract

witheachothertoformcomplexAIsystems.Additionally,AIsystemsareoftendesignedtointeractwithothersystemsforsharingdata,

facilitatingseamlessintegrationintoreal-worldenvironments.ThisresultsinanetworkofAIsystems,eachwithitsspecializedmodels,workingtogethertoachievealargergoal.

WithAIpoisedtorevolutionisemanyaspectsofourlives,freshcooperativegovernanceapproachesareessential.Effectivecollaborationbetweenregulatoryportfolios,withinnationsaswellasacrossborders,iscrucial:bothtosafeguardpeoplefromharmandtofosterinnovationandgrowth.

KateJones

U.K.DigitalRegulationCooperationForumCEO

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→PartI.UnderstandingAIandgovernance

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NavigatingAIgovernancesources

Giventhecomplexityandtransformative

natureofAI,significantworkhasbeendonebylawandpolicymakersonwhatisnowa

vastandgrowingbodyofprinciples,laws,

policies,frameworks,declarations,voluntarycommitments,standardsandemergingbestpracticesthatcanbechallengingtonavigate.Manyofthesevarioussourcesinteractwitheachother,eitherdirectlyorbyvirtueoftheissuescovered.

AIprinciples,suchastheOECD'sAIPrinciplesorUNESCO'sRecommendationontheEthics

ofAI,canshapeglobalstandards,especiallywhennationalgovernmentspledgeto

voluntarilyincorporatesuchguidanceinto

theirdomesticAIgovernanceinitiatives.

Theyprovideanonbinding,principled

approachtoguidelegal,policyandindustry

effortstowardtacklingthematicchallenges.AlgorithmWatchcreatedaninventoryoftheseprinciples,identifying167

reports

.

Lawsandregulationsincludeexistinglegislationthatisnotspecificbutis

nonethelessapplicabletoAI,aswellas

emerginglegislationthatmorespecifically

addressesthegovernanceofAIsystems,such

astheEUAIAct.TheEUAIActistheworld's

firstcomprehensiveAIregulation.Although

jurisdictionalvariationscanbeobservedacross

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theemergingglobalAIregulatorylandscape,manydraftregulationsadoptarisk-based

approachsimilartotheEUAIAct.

TheEUAIActmandatesAIgovernancestandardsbasedontheriskclassificationofAIsystemsandtheorganization'sroleasanAIactor.Certain

AIsystemsaredeemedtoposeunacceptable

riskandareprohibitedbylaw,subjecttovery

narrowexceptions.Thebulkoftherequirementsimposedbytheactapplytoprovidersofhigh-riskAIsystems,althoughdeployersandresellers,

namelydistributersandimporters,arearealsosubjecttodirectobligations.

Theactimposesregulatoryobligationsat

enterprise,productandoperationallevels,

suchasestablishingappropriateaccountabilitystructures,assessingsystemimpact,providingtechnicaldocumentation,establishingrisk

managementprotocolsandmonitoring

performance,amongotherkeyrequirements.Inthecontextofthegrowingvarietyof

generativeAIusecasesandadoptionof

solutionsembeddinggenerativeAIsuchasMSCopilot,generalpurposeAI-specificprovisionsareanothercrucialcomponentoftheEUAI

Act.Dependingontheircapabilities,reachandcomputingpower,certainGPAIsystemsare

consideredtopresentsystemicriskandattractbroadlysimilarobligationstothoseapplicabletohigh-riskAIsystems.

AIgovernanceisabouttogetalot

harder.Theinternalcomplexityof

governingAIisgrowingasmore

internalteamsadoptAI,newAI

featuresarebuilt,andthesystems

getcomplex,butatthesametime,

theexternalcomplexityisalsosetto

growrapidlywithnewregulations,

customerdemands,andsafety

researchevolving.

Theorganizationswhohaveinvested

instructuredAIgovernancealready

havealegupandwillcontinueto

haveacompetitiveadvantage.

AndrewGamino-Cheong

TrustibleAICo-founderandChiefTechnologyOfficer

AIGovernanceinPracticeReport2024|7

Inadditiontobindinglegislation,voluntaryAIframeworks,suchastheNationalInstitute

ofStandardsandTechnology'sAIRisk

IAPPGlobalAILawandPolicyTracker

ManagementFrameworkandtheInternationalOrganizationforStandardization'sAIStandards,

offerstructuredandactionableguidance

stakeholderscanelecttousetosupport

theirworkonimplementingAIgovernance.

Voluntarycommitmentsareoftendevelopedtobringdifferentstakeholdersclosertoasharedunderstandingofidentifying,assessingand

managingrisks.Standardsserveasbenchmarks

thatcandemonstratecompliancewithregulatoryrequirements.

Internationaldeclarationsandcommitments

memorializesharedcommitments,oftenbetweengovernments,tospecificaspectsorbroad

swathesofAIgovernance.Whilenotbinding,

suchcommitmentscan,ataminimum,indicateacountry'ssupportforandintentiontoadvanceAIgovernanceinparticularorgeneralways,evenatthehighestoflevels.

NavigatingagrowingbodyofdraftAIlaws,

regulations,standardsandframeworkscan

bechallengingfororganizationspioneering

Thismapshowsthejurisdictionsinfocusandcoveredbythe

IAPPGlobalAILawandPolicyTracker

.ItdoesnotrepresenttheextenttowhichjurisdictionsaroundtheworldareactiveonAIgovernancelegislation.TrackerlastupdatedJanuary2024.

withAI.ByunderstandingtheiruniqueAIriskprofileandadoptingarisk-basedapproach,

organizationscanbuildarobustandscalableAIgovernanceframeworkthatcanbedeployedacrossjurisdictions.

AIGovernanceinPracticeReport2024|8

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Thefollowingare

examples

ofsomeofthemostprominentandconsequential

AIgovernanceefforts

:

Principles

→OECDAIPrinciples

→EuropeanCommission'sEthicsGuidelinesforTrustworthyAI

→UNESCORecommendationontheEthicsofAI

→TheWhiteHouseBlueprintforanAIBillofRights→G7HiroshimaPrinciples

→EUAIAct

Lawsand

→EUProductLiabilityDirective,proposed→EUGeneralDataProtectionRegulation→Canada–AIandDataAct,proposed

→U.S.AIExecutiveOrder14110

regulations

→SectoralU.S.legislationforemployment,housingandconsumerfinance→U.S.statelaws,suchasColoradoAIAct,SenateBill24-205

→China'sInterimMeasuresfortheManagementofGenerativeAIServices

→TheUnitedArabEmiratesAmendmenttoRegulation10toincludenewrulesonProcessingPersonalDatathroughAutonomousandSemi-autonomousSystems

→DigitalIndiaAct

AIframeworks

→OECDFrameworkfortheclassificationofAISystems→NISTAIRMF

→NISTSpecialPublication1270:TowardsaStandardforIdentifyingandManagingBiasinAI→SingaporeAIVerify

→TheCouncilofEurope'sHumanRights,Democracy,andtheRuleofLawAssuranceFrameworkforAIsystems

Declarations

→BletchleyDeclaration

andvoluntary

→TheBiden-HarrisAdministration'svoluntarycommitmentsfromleadingAIcompanies

commitments

→Canada'sguideontheuseofgenerativeAI

→ISO/IECJTC1SC42

Standardsefforts

→TheInstituteofElectricalandElectronicsEngineersStandardsAssociationP7000

→TheEuropeanCommitteeforElectrotechnicalStandardization

AIstandardsforEUAIAct

→The

VDEAssociation'sAIQualityandTestingHub

→TheBritishStandardsInstitutionand

AlanTuringInstituteAIStandardsHub

→Canada's

AIandDataStandardsCollaborative

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Itisimportanttotakean

ecosystemapproachtoAI

governance.Policymakersand

industryneedtoworktogether

atplatformssuchastheAIVerify

Foundationtomakesenseofthe

opportunitiesandrisksthatthis

technologybrings.Theaimisto

findcommonguardrailstomanage

keyrisksinordertocreatea

trustedecosystemthatpromotes

maximalinnovation.

DeniseWong

SingaporeInfocommMediaDevelopmentAuthorityAssistantChiefExecutive,DataInnovation&ProtectionGroup

AIGovernanceinPracticeReport2024|9

AIRisks

REPUTATIONAL

Riskof

damageto

reputation

andmarket

competitiveness

INDIVIDUALSANDSOCIETY

Riskofbias

orother

detrimental

impacton

individuals

LEGALANDREGULATORY

Riskof

noncompliance

withlegaland

contractual

obligations

FINANCIAL

Riskoffinancial

implications,

e.g.,fines,legal

oroperational

costs,or

lostprofit

TheAIgovernanceimperative

Withprivateinvestment,globaladoptionratesandregulatoryactivityontherise,aswellas

thegrowingmaturityofthetechnology,AIis

increasinglybecomingastrategicpriorityfororganizationsandgovernmentsworldwide.

Organizationsofallsizesandindustriesare

increasinglyengagingwithAIsystemsatvariousstagesofthetechnologyproductsupplychain.

Theexceptionaldependenceonhighvolumes

ofdataandendlesspracticalapplicabilitythat

makeAItechnologyadisruptiveopportunity

canalsogenerateuniquelymultifacetedrisksforbusinessesandindividuals.Theseincludelegal,regulatory,reputationaland/orfinancialriskstoorganizations,butalsoriskstoindividualsandthewidersociety.

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Enterprisegovernance

AIgovernancestartswithdefiningthecorporatestrategyforAIbydocumenting:

→TargetoperatingmodelstosetoutclearrolesandresponsibilitiesforAIrisk.

→Complianceassessmentstoestablish

programmaturityandremediationpriorities.

→Accountabilityprocessestorecordanddemonstratecompliance.

→Policiesandprocedurestoformulatepolicystandardsandoperationalprocedures.

→Horizonscanningtoenhanceandaligntheprogramwithongoingregulatorydevelopments.

Productgovernance

AIgovernancealsorequiresenterprisepolicystandardstobeappliedattheproductlevel.OrganizationscanensuretheirAIproductsmatchtheirenterprisestrategybyusing:

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→Systemimpactassessmentstoidentifyandaddressriskpriortoproductdevelopmentordeployment.

→Qualitymanagementprocedurestailoredtothesoftwaredevelopmentlifecycletoaddressriskbydesign.

→Riskandcontrolsframeworkstodefine

AIriskandtreatmentbasedonwidely

recognisedstandardssuchasISOandNIST.

→Conformityassessmentsanddeclarationstodemonstratetheirproductsarecompliant.

→Technicaldocumentationincludingstandardizedinstructionsofuseandtechnicalproductspecifications.

→Post-marketmonitoringplanstomonitor

productcompliancefollowingmarketlaunch.

→Third-partyduediligenceassessmentstoidentifypossibleexternalriskandinformselection.

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Operationalgovernance

Theorganization'sAIstrategymustultimatelybeoperationalizedthroughoutthebusinessthroughthedevelopmentof:

→Performancemonitoringprotocolstoensuresystemsperformadequatelyfortheirintendedpurposes.

→Transparencyandhumanoversightinitiativestoensure

individualsareawareandcanmakeinformedchoiceswhentheyinteractwithAIsystemsorwhenAI-powereddecisionsaremade.

→Incidentmanagementplanstoidentify,escalateandrespondtoseriousincidents,malfunctionsandnationalrisksimpactingAIsystemsandtheiroperation.

→Communicationstrategiestoensuretransparencytowardinternalandexternalstakeholdersinrelationtothe

organization'sAIpractices.

→TrainingandawarenessprogramstoenablestaffwithrolesandresponsibilitiesforAIgovernancetohelpthemunderstandandperformtheirrespectiveroles.

→Skillsandcapabilitiesdevelopmenttoassesshumanresourcescapabilitiesandreviewordesignjobrequirements.

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TheAIlifecycle

PLANNING

→Plananddocumentthesystem'sconceptandobjectives.

→Planforlegaland

regulatorycompliance.

DESIGN

→Gatherdataandcheckfordataquality.

→Documentand

assessmetadataandcharacteristicsof

thedataset.

→Considerlegaland

regulatoryrequirements.

DEVELOPMENT

→Selectthealgorithm.→Trainthemodel.

DEPLOYMENT

→Pilotandperform

compatibilitychecks.

→Verifylegaland

regulatorycompliance.

→Monitorperformanceandmitigateriskspostdeployment.

→Carryouttesting,

validationand

verification.

→Calibrate.

→Carryoutoutput

interpretation.

UnderstandingthatAIsystems,likeall

products,followalifecycleisimportantastherearegovernanceconsiderationsacrossthelifecycle.The

NISTAIRMF

setsouta

comprehensivearticulationoftheAIsystem

lifecycleandincludesconsiderationsfor

testing,evaluation,validation,verification

andkeystakeholdersforeachphase.Amoresimplifiedsamplelifecycleisincludedabove,alongwithsometop-levelconsiderations.

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AneffectiveAIgovernance

modelisaboutcollective

responsibilityandcollective

businessresponsibility,

whichshouldencompass

oversightmechanismssuch

asprivacy,accountability,

compliance,amongothers.

Thisresponsibilityshouldbe

sharedbyeverystakeholder

whoispartoftheAI

governancechain.

VishalParmar

BritishAirwaysGlobalLeadPrivacyCounselandDataProtectionOfficer

AIGovernanceinPracticeReport2024|13

→PartI.UnderstandingAIandgovernance

TABLEOFCONTENTS↑

→HOWTO

Navigatedevelopersfromdeployers

Variouscontractualandregulatoryobligationsmayarise

dependingonwhetheranorganizationisavendororbuyer,

orifitsourcesexternalservicessuchashardware,cloudordatacollection,forthedevelopmentandoperationsofitsAIsystem.

PriorIAPP

research

foundmorethan70%oforganizationsrelyatleastsomewhatonthird-partyAI,sotheresponsibilityforensuringtheAIsystemissafeandresponsiblemaybe

spreadacrossmultipleroles.

→TheWorldEconomicForumputtogetherauseful

toolkit

tohelpthosewhoareprocuringAIsystems.

Inboth

currentlegislation

and

proposedlegislation

wearestartingtoseedifferentobligationsforthosewhoprovideandsupplyAIversusthosewhodeployAI.Understandingwhetheryouareadeveloperand/ordeployeris

importanttoensuringyoumeetcomplianceobligations.Oncethisisunderstood,itispossibletoestablishAI-governanceprocessesfor

procurement

,includingevaluationsandcontractstoavoidtakingonadditionalliabilities.

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PartII.Thedatachallenge

DataisanintegralpartoftrainingandoperatinganAIsystem.

MostAIrequiressizeableamountsofhigh-qualitydata,

especiallyduringthetrainingphasetomaximizethemodel'sperformance,aswellastoensurethedesiredandaccurate

output.WiththeadvancementofnewAItechnologies,modelsarerequiringincreasinglymoredata,whichmaycomefromavarietyofsources.GiventheimportanceofthedatausedtofueltheAIsystem,itisimportanttounderstandwhatdataisbeingused;how,whereandbywhomitwascollected;from

whomitwascollected;ifitistherightdataforthedesired

outcome;andhowitwillbemanagedthroughoutthelifecycle.

Accessingdataandidentifyingdatasources

Understandingwheredatacomesfromandhowitiscollectedis

notonlynecessaryforAIsystems,butalsoforbuildingtrustinAIbyensuringthelawfulnessofdatacollectionandprocessing.Suchdocumentationcanassistwith

datatransparency

andimprovetheAIsystem'sauditabilityaswell.

Althoughdatamayoriginatefrommultiplesources,itcanbebroadlycategorizedintothreetypes:first-partydata,publicdataandthird-partydata.

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First-partydata

Thisreferstodatacollecteddirectlyfrom

individualsbyanorganizationthroughtheirowninteractionsandtransactions.Suchdatamay

originatefromsourcessuchaswebsitevisits,

customerfeedbackandsurveys,subscriptions,andcustomerrelationshipmanagementsystems,amongothers.Thisdataisextremelyvaluablefororganizationsasitprovidesdirectandfirsthandinsightsintoindividuals'behavior.

First-partydatacanbecollectedfromvarious

sources.Identifyingthedatachannelsand

documentingthesourcewillnotonlyhelpthe

organizationdeterminewhattypesofdata,e.g.,text,numerical,imageoraudio,willbecollectedfromeachsource,butalsoalertthelegalteam

aboutwherelegalcompliancewillberequiredontheorganization'spart.

Publicdata

Thisreferstodatathatisavailabletothewider

publicandencompassesarangeofsources,

suchaspubliclyavailablegovernmentrecords,publications,andopensourceandweb-scrapeddata.Publicdataisavaluableresourcefor

researchersandinnovatorsasitprovidesreadilyavailableinformation.Publicdatacancome

frommultiplesources.

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Whileitisarduousandcumbersometo

maintaindatalineageforpublicdatasets,

itisimportantforupholdingorganizational

reputationandfosteringusertrust,legal

complianceandAIsafetyoverall.Alackof

understandingofwheredatacomesfrom

eventuallyleadstoalackofunderstandingofthetrainingdatasetandmodelperformance,whichcanreinforcetheblack-boxproblem.

Therefore,intheinterestoftransparency,

trackinganddocumentingpublic-datasourcesasmuchaspossiblemayprovebeneficialfortheorganization,asitcanlatersupportothertransparencyefforts,suchasdrawingupdata,modelorsystemcards.

Moreover,withoutknowledgeofpublic-datasources,theorganizationmayinadvertentlytraintheAIsystemonpersonal,sensitive

orproprietarydata.Fromtheprivacy

standpoint,thiscanbeproblematicincasesof

dataleakage

,wherepersonallyidentifiabledatamaybeexposed.AIsecuritychallengesmayalsobeamplifiedifdatawasprocured

fromunsafepublicsources,asthatcarriestheriskofintroducingmaliciousbugsintothesystem.Itmayalsoleadtobiasesin

theAIsystem.

Ethicaldevelopment

practicesstartwith

responsibledata

acquisitionand

managementsystems,

aswellasreview

processesthattrack

thelineageof

sourceddata.

ChristinaMontgomery

IBMVicePresidentand

ChiefPrivacyandTrustOfficer

AIGovernanceinPracticeReport2024|16

Withoutunderstandingthequality

ofthedatabeingingestedintoan

AImodel,youmaynotknowthe

qualityoftheoutput.Companies

mustestablishanddefinewhat‘data

quality’involvesandconsistsof,asthis

determinationishighlycontextualfor

anyorganization,andcandependon

businessgoals,usecases,focusareas

andfitnessforpurpose.

Regardlessofcontext,thereare

minimumbaselineattributeswhichcan

andshouldbeestablished:accuracy,

completeness,consistencyand

validity.Timelinessanduniqueness

mayalsobeimportanttoestablishing

fitnessforpurpose.

DeraNevin

FTITechnologyManagingDirector

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Anorganizationcanbeginbyestablishinga

clearunderstandingofhowandwhypublic

dataisbeingcollected,howitalignswiththe

purposestheAIsystemwillfulfil,ifandhow

systemaccuracywillbeaffectedbyusing

publicdata,whatthetrustworthysourcesfor

gatheringpublicdataare,iftheorganization

hasrightstousethepublicdata,andotherlegalconsiderationsthatmayhavetobetakeninto

account,particularlygiventhatpublicdataistreateddifferentlyacrossjurisdic