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arXiv:2303.15715v1[cs.CY]28Mar2023
FOUNDATIONMODELSANDFAIRUSE
APREPRINT
PeterHendersonXuechenLiDanJurafsky,TatsunoriHashimoto,MarkA.Lemley,PercyLiangStanfordUniversity
March29,2023
ABSTRACT
Existingfoundationmodelsaretrainedoncopyrightedmaterial.Deployingthesemodelscanposebothlegalandethicalriskswhendatacreatorsfailtoreceiveappropriateattributionorcompensation.IntheUnitedStatesandseveralothercountries,copyrightedcontentmaybeusedtobuildfoundationmodelswithoutincurringliabilityduetothefairusedoctrine.However,thereisacaveat:Ifthemodelproducesoutputthatissimilartocopyrighteddata,particularlyinscenariosthataffectthemarketofthatdata,fairusemaynolongerapplytotheoutputofthemodel.Inthiswork,weemphasizethatfairuseisnotguaranteed,andadditionalworkmaybenecessarytokeepmodeldevelopmentanddeploymentsquarelyintherealmoffairuse.First,wesurveythepotentialrisksofdevelopinganddeployingfoundationmodelsbasedoncopyrightedcontent.WereviewrelevantU.S.caselaw,drawingparallelstoexistingandpotentialapplicationsforgeneratingtext,sourcecode,andvisualart.Experimentsconfirmthatpopularfoundationmodelscangeneratecontentconsiderablysimilartocopyrightedmaterial.Second,wediscusstechnicalmitigationsthatcanhelpfoundationmodelsstayinlinewithfairuse.Wearguethatmoreresearchisneededtoalignmitigationstrategieswiththecurrentstateofthelaw.Lastly,wesuggestthatthelawandtechnicalmitigationsshouldco-evolve.Forexample,coupledwithotherpolicymechanisms,thelawcouldmoreexplicitlyconsidersafeharborswhenstrongtechnicaltoolsareusedtomitigateinfringementharms.Thisco-evolutionmayhelpstrikeabalancebetweenintellectualpropertyandinnovation,whichspeakstotheoriginalgoaloffairuse.Butweemphasizethatthestrategieswedescribeherearenotapanaceaandmoreworkisneededtodeveloppoliciesthataddressthepotentialharmsoffoundationmodels.
1Introduction
Foundationmodels
2
thataretrainedonlarge-scaleinternetdataserveasthebaseforanincreasingnumberofdeployedapplicationsintherealworld(
Bommasanietal.
,
2021
).ModelssuchasGPT-3/4(
Brownetal.
,
2020
;
OpenAI
,
2023
),
StableDiffusion(Rombachetal
.,
2021
),andCodex(
Chenetal.
,
2021
)areactivelybeingintegratedintoavarietyofproductslikeDuolingo’sLanguageLearningApp,
3
StabilityAI’sDreamStudio,
4
GitHub’sCoPilot,
5
andmore.Researchersaregrapplingwiththelegalityandethicsofdevelopinganddeployingthesemodelsusingdatabroadlycollectedfromtheinternet.Manyhaveraisedconcernsaboutusinguncuratedinternetdataformodeldevelopment,touchingonissuesofprivacy(
Carlinietal.
,
2021
)andfairness(
Benderetal.
,
2021
).Butasfoundationmodelsaredeployedinwaysthatcanharmthemarketsoftheoriginaldatacreators,particularlywhengeneratingcontentsimilartotheoriginaldata,intellectualpropertyrightsbecomeagrowingconcern.Inthispaper,westudythelegalchallengesofbuildinganddeployingfoundationmodelsfromtheperspectiveofintellectualproperty,focusingmainlyoncopyright.
*EqualContribution.Correspondenceto
phend@
,
lxuechen@
.©PeterHenderson,XuechenLi,DanJurafsky,TatsunoriHashimoto,MarkA.Lemley,&PercyLiang
2Foundationmodelscanroughlybedefinedaslargepre-trainedmachinelearningmodelsthatareusedasastartingpointforvariouscomputationaltasks.
3
/duolingo-max/
4
https://stability.ai/
5
/features/copilot
FoundationModelsandFairUseAPREPRINT
2
UnderUnitedStates("U.S.")law,copyrightforapieceofcreativeworkisassigned“themomentitiscreatedandfixedinatangibleformthatitisperceptibleeitherdirectlyorwiththeaidofamachineordevice”(
U.S.CopyrightOffice
,
2022
).Thebreadthofcopyrightprotectionmeansthatmostofthedatathatisusedfortrainingthecurrentgenerationoffoundationmodelsiscopyrightedmaterial.Forexample,
Bandy&Vincent
(
2021
)pointedoutthattheBookCorpuscontainscopyrighteddataunderrestrictivelicensesandhasbeenusedtotrainlargefoundationmodelsincludingGPT-3(
Brownetal.
,
2020
)andBERT(
Devlinetal.
,
2018
).
Similarly,ThePile(Gaoetal
.,
2020
)containsBooks3,adatasetofcopyrightedandcommerciallysoldbooksdownloadedfromBibliotik,atorrenttrackerforbooksandlearningmaterials(
Presser
,
2020
;
Bidermanetal.
,
2022
).Moregenerally,mostfoundationmodelsaretrainedondataobtained
fromwebcrawlslikeC4(Raffeletal
.,
2019
)orOpenWebText(
Gokaslan&Cohen
,
2019
).Sincemostonlinecontenthascopyrightprotectionsattachedatcreation,usingthemforcertainpurposescouldbeconsideredinfringement.
6
Researchers,atleastintheUnitedStates,havelongreliedonthelegaldoctrineoffairusetoavoidliabilityfromusingcopyrighteddata.Fairuseallowsthepublictousecopyrightedmaterialforcertaintypesofpurposes—evenwithoutalicense—especiallywhentheend-productistransformative.Forexample,whenreleasingpotentiallycopyrightedcontentinthepast,individualsandorganizationshavereliedonroughguessesforwhatconstitutesfairuse.Acommonapproachistoreleasesnippets:5-grams(
PublicResource
,
2021
),11-grams(
Brown&Mercer
,
2013
),orseveralpages(
AuthorsGuild,Inc.v
.Google,Inc.,
2dCir.2015
).
Lemley&Casey
(
2020
)havepointedoutthattrainingamachinelearningmodeloncopyrighteddataislikelyconsideredfairuseincircumstanceswherethefinalmodeldoesnotdirectlygeneratecontent.Forexample,trainingamodelonacorpusofpopularbookssolelyforpredictingthesimilarityoftwopassagesistransformativeandlikelyfallsunderfairuse.
7
However,whenitcomestotraininganddeployingfoundationmodelsforgenerativeusecases,theanalysisbecomesmorecomplex.Thisisbecausethesemodelsareusuallycapableofgeneratingcontentsimilartocopyrighteddata,anddeployingthemcanpotentiallyimpacteconomicmarketsthatbenefittheoriginaldatacreators.Forthesescenarios,legalscholarsarguethatfairusemaynotapply(
Lemley&Casey
,
2020
;
Sobel
,
2017
;
Levendowski
,
2018
).
Byexpandingthecapabilitiesofmodels,machinelearningresearchersandpractitionershavestumbledintothemuddywatersoffairuse.Asaresult,websiteslikeGettyImageshavebannedAI-generatedcontent(
Vincent
,
2022
),andlawsuitshavebeenfiledagainstproductsusingfoundationmodels,namelyGitHubCopilotandStableDiffusion(
DOE
1v
.GitHub,Inc.,
N.D.Cal.2022
;
Andersenetal.v
.StabilityAIetal.,
N.D.Cal.2023
;
Vincent
,
2023
).Inthiswork,weshedlightonthissubjectmatterformachinelearningresearchersandhighlightthatsignificantadditionalworkisrequiredtode-riskfoundationmodeldeploymentsforgenerativeusecases,focusingprimarilyonU.S.laws.
First,weprovideanoverviewofU.S.caselawonthefairusedoctrine.
8
Wedrawanalogiestofoundationmodelusecases.Wesupplementthesewithareviewofpriorexperiments,aswellasnovelexperiments,andillustratethatfoundationmodelscanproducecontentthatissufficientlysimilartocopyrightedmaterial.Furthermore,thecaselawsuggeststhatevencertaintypesoftransformationsofthetrainingdatawouldnotbeconsideredfairuse.Thus,theriskofinfringementisreal,andfairusewillnotcovereveryscenariowhereafoundationmodeliscreatedorused.Theexactamountofriskisunclear,andthelawwillevolvewithongoinglitigation.
Second,weoverviewtechnicalmitigationstrategiesthatwillreducethisriskinaccordancewiththecurrentstateofthefairusedoctrine.
Grimmelmann
(
2015
)statedthat“payingattentiontoroboticreadershiprefocusesourattentiononthereallyfundamentalquestions:whatiscopyright,andwhatisitfor?Tosaythathumanreaderscountandrobotsdon’tistosaysomethingdeepaboutthenatureofreadingasasocialpractice,andaboutwhatwewantrobots—andhumans—tobe.”
Lemley&Casey
(
2020
)suggestedthathumansandAIshouldbeheldtosimilarstandardswhenitcomestocopyright.Ifthisisthecase,itisthejobofmachinelearningresearchersandpractitioners,workingtogetherwithlegalpractitioners,toensurethatfoundationmodelscreatetransformativecontentwhichwouldpassmusterunderthesamefairuseanalysisasprovidedtoahuman.Togetthere,newstrategiesandtechniqueswillneedtobedeveloped,takingstepstoensurethatfoundationmodelsbehaveinmoretransformativeandnovelways.Wecallformoreresearchtoaligntechnicalmitigationstrategieswithfairuse,includingbetteroutputfilteringmechanismsrelyingonhigher-levelsemanticsandnewinnovationintraining-timetechniqueslikeextraction-preventativelearningfromhumanfeedback.Developingthesemitigationstrategiesisanimportantresearchchallengeformachinelearningandnaturallanguageprocessingandwouldbringpracticesinthetwofieldsintobetteralignmentwiththelaw.
6Wenotethattherearenuancestoeventheinfringementpoint,sincesomeusesthatrespectrobots.txtspecificationsmighthaveanimpliedlicenseasdescribedin
Fieldv
.Google,Inc.(
D.Nev.2006
).Thisisunlikelytoapplytoallgeneratedmodeloutputs,however,andwediscussthisfurtherin§
4.1
.
7Thoughrecentlitigationpointsoutthatnocourthasactuallyweighedinonthematterofwhethermodeltrainingisfairuse(
DOE
1v
.GitHub,Inc.,
N.D.Cal.2022
,Complaintat23).
8WeexamineU.S.fairusedoctrine,ratherthaninternationaldoctrines,fortworeasons.First,companieshavespecificallypointedtofairuseasadefensefortheiruseoffoundationmodels.Forexample,formerGithubCEONatFriedmanpointedtofairusewhenreferringtoGithub’sCopilotdeployment.See
/natfriedman/status/1409914420579344385
Second,theexpertiseoftheauthorsisinU.S.law.
FoundationModelsandFairUseAPREPRINT
3
Lastly,wearguethataco-evolutionoftechnicalmitigationstrategiesandlawcanhelpestablishamiddlegroundwherethepositiveimpactoffoundationmodelsisrealizedwhilereducingtheharmstodatacreators’intellectualpropertyrights.Withthecurrentuncertaintiesoffairusedoctrine,as
Sobel
(
2017
)andothersnoted,thelawmayswaytooneextremeoranother.OnonehanditcouldleadtooverlypermissiveinterpretationsoffairusethatcouldallowanygenerativeAIuse,disregardingtherightsofdatacreators.Oritcouldleadtooverlyrestrictiveinterpretationsoffairusethatcouldbroadlypreventfoundationmodeltraininganduse,concentratingpoweramongentitiesthathavealreadyacquiredvastquantitiesoflicenseddata.Bydevelopinganddeployingstrongtechnicalmitigationstrategies,itmaybepossibletolessentheriskofsuchextremelegaloutcomes.Andthelawshouldtakeintoaccounttheexistenceandstrengthofsuchtechnicalmitigationstrategies.Thiscouldinvolveamulti-prongedapproach:consideringtechnicalmitigationsinfairuseassessments,clarifyingthestatusofDMCAprotectionsforfoundationmodels,ordevelopingDMCA-likesafeharborsfordeploymentsthatusestrongtechnicalmitigationefforts,pursuingpolicystrategiesforreducingharmstolabor,andmore.Realizingthismiddlegroundrequirestheparticipationofamuchbroadercommunityincludingthedatacreatorsimpactedbyfoundationmodels,technologists,legalprofessionals,amongmanyothers.Weencouragemoremultidisciplinaryworktofurthertheco-evolutionoflaw,policy,andtechnicalmethodsformitigatingintellectualpropertyharms.
Overall,thegoalofthisworkistoactbothasaguideandcall-to-actionforMLresearchersandpractitionerstoactivelypursuetechnicalmitigationstrategies.Wehopethatthisguidehelpsinstillabetterunderstandingthatfairuseisnotapanacea,andthatanuancedcomprehensionofthelegallandscape’sintricaciesisvitaltoeffectivelynavigatepotentialpitfallsanduncertainties.Furthermore,thisworkmayalsoproveusefultolawyersandpolicymakers,providingthemwithmoreinsightintopotentialtechnicaldetailsoffoundationmodels,includingtechnicalmitigationstrategies,andhowtheymightplayaroleinthedevelopinglegalbestpracticesandpotentialreforms.
2FoundationModelsandFairUse
Wefirstbrieflydefinefoundationmodelsandintroducefairuselawaswellasitsapplicabilitytofoundationmodels.Toprovideabetterunderstandingoftherisks,wethenexamineconcreteprecedentialcasesrelatedtofairuseandhowtheymightapplytofoundationmodels.Weconductthisanalysisforcasesrelatedtotext,code,andvisualart.ToaccompanyourexaminationofU.S.caselaw,weincludehypotheticalscenariosofmodeldeploymentsandhowtheymightexceedtheboundsofthefairusedoctrineundercurrentlaw.Wealsoprovideexperimentstoshowthatcurrentfoundationmodelsarecapableofgeneratingcontentthatisnottransformative.
Thissectionproceedsasfollows.Section
2.1
providesabriefoverviewoffoundationmodels.Section
2.2
providesdefinitionsofactorsinvolvedinthefoundationmodeldevelopmentanddeploymentprocessandwhatrolestheyplay.Section
2.3
providesahigh-leveloverviewoffairusedoctrineintheUnitedStates.Sections
2.4
,
2.5
,and
2.6
providein-depthexamplesofcaselawandfoundationmodelscenariostohelpelucidatepotentialrisks.
2.1FoundationModels
Foundationmodelsaremachinelearningmodelstrainedonbroaddata(typicallyscrapedfromtheinternet)generallyusingself-supervisionatscale(
Bommasanietal.
,
2021
).Mostfoundationmodelsarenottrainedtoaccomplishspecifictasksbutrathertocaptureusefulgeneralinformationinthedata.Forinstance,mostautoregressivelypretrainedlanguagemodels(e.g.,GPT-3(
Brownetal.
,
2020
),PaLM(
Chowdheryetal
.,
2022
),orChinchilla(
Hoffmannetal.
))aretrainedtopredictthenextwordgivenasequence.Mosttext-to-imagemodels,forexampleDALL·
E(Rameshetal
.,
2021
),aretrainedtocapturethedistributionofimagesgivenatextprompt.Thesemodelscanthenbetunedtoalignmorewithhumanpreferences(
Ouyangetal
.,
2022
)orbeadaptedforspecifictasks.Foundationmodelscanbeusedforgeneratingcontent.ThisincludesmodelslikeGPT-3(
Brownetal.
,
2020
)fortext,Codex(
Chenetal.
,
2021
)forcode,andDALL·
E(Rameshetal
.,
2021
)forimages.Alternatively,theycanbeusedfornon-generativepurposes.Thesewouldtypicallyoutputonevalue,ratherthanhavingalongerfree-formoutput.Forexample,theymightclassifytextindifferentways,orpredictanumericalvaluefromanimage.Thisincludes(forthemostpart)modelslikeBERT(
Devlin
etal.
,
2018
)orCLIP(
Radfordetal.
,
2021
).Importantly,mostfoundationmodelscanbemodifiedtooperateforeithertypeoftask,andmanytaskswillbesomewhereonthespectrumbetweengenerativeandnon-generativetasks.
9
9Thisspectrumbetweengenerativeandnon-generativetasksisimportanttounderstandasitmayhavesomeimpactonthefairuseanalysisandwediscusshowtechnicalmitigationstrategiescantakethisintoaccountinSection
4.1
.
FoundationModelsandFairUseAPREPRINT
4
Millionsofusersnowusefoundationmodelproducts.ChatGPT,ageneralistchatbotfromOpenAI,hasgrowntoanestimated100Mdailyactiveusers.
10
Midjourney’susersproducemillionsofgeneratedimagesperday.
11
Asfoundationmodelsareexpandedintomoreproducts,deploymentswillonlyscaletomoreandmoreusers.AnincreasinglygrowinglistofcompanieshasplanstodeploysimilarproductstoChatGPT,fromMicrosoft’sBingChat
12
toGoogle’sBard,
13
andmore.Wecategorizethehigh-profileinstancesbythedomainofthedatainTable
1
.
DomainProducts
Text
GeneralPurposeAPI(e.g.,
OpenAIGPTAPI
)orgeneralchat-basedagents(e.g.,
ChatGPT
)
Writeblogsandmarketingmaterial(e.g.,
copy.ai
)
Customgeneratedstories(e.g.,
/
)
Text-basedadventuregames(e.g.,
https://aidungeon.io/
)
Code
Generatecode(e.g.,
GithubCoPilot
)
PairprogrammingwithanAIassistant(e.g.,
Replit
)
Images
Generateimagesfromtext(e.g.,
OpenAIDall-E
,
AzureOpenAIService
,
MicrosoftDesigner
,
StableDiffusion
,
Midjourney
)
Table1:WeenumerateasmallfractionofadvertisedfoundationmodeldeploymentsandproductsprovidedviaAPIsorotherinterfaces,demonstratingthatthesesystemsarebeingdeployedasproductsinawiderangeofareas.
2.2DefinitionsandRoles
Beforeourdiscussion,wedefineseveralactors.Thedatacreatorcreatesdatathatamodelmightbetrainedon.Thedatacuratorcollectsdataandadatahostdistributesdatathatamodelistrainedon.Themodelcreatortrainsthemodelonthisdata.ThemodeldeployerhostsamodelandprovidesaccesstoitviaanAPI,potentiallycreatingrevenuefromservingthemodel.Themodeluserusesthemodelfordownstreamtasks,potentiallycreatingrevenuewiththeoutputofthemodel.Theseactorsmayallbethesamepersonorentity,ortheymaybedifferentpeopleorentities.
Weprimarilydiscussthepotentialforadataintellectualproperty(IP)owner(thedatacreator)tobringacaseagainstfoundationmodeldeployers,users,andcreators.Whilethereiscertainlyrisksofliabilityfordatacurators,thishaslongbeendiscussedinotherwork.Wewillalsofocusonliabilityasaresultofthemodeloutputsthemselves,notthetrainingprocessorthemodelparameters.
14
Instead,wefocusonwhetherthoseweightscanbeusedinaninfringingwayandthusincurliability.
2.3FairUse
IntheUnitedStates,thelegaldoctrineoffairuseprovidessomerelieffromliabilityforusingcopyrightedmaterialwithoutalicense.Thefairusedefenseisdeterminedbyconsideringfourfactors:(1)thepurposeandcharacteroftheuse,includingwhethertheuseisofacommercialnatureorisfornonprofiteducationalpurposes(transformativeness);(2)thenatureofthecopyrightedwork(fairusestronglyfavorediforiginalworkisfactualasopposedtocreative);(3)theamountandsubstantialityoftheportionusedinrelationtothecopyrightedworkasawhole;(4)theeffectoftheuseuponthepotentialmarketfororvalueofthecopyrightedwork.See17U.S.C.§107.Itisimportanttonotethateveryfactorwillplaysomeroleinthecourt’sdecision-makingprocess,buttheinteractionbetweenthemisnotalwaysclear.
Wewillbrieflyprovideanoverviewofeachfairusefactorinthissection,butwestressthatfairusedoctrineismurkyandevolving.Inanycommonlawsetting,acase-by-casereviewhelpsoutlinethecontoursofthedoctrine,sowewillsubsequentlyreviewrelevantcaselawtohelpshinealightonhowfairusedoctrinemighthandlefoundationmodels.Withinthetopicswediscuss,weprovideadescriptivesurveyofthecurrentstateoffairusedoctrineandhowitcouldrelatetofoundationmodelstotheextentpossible.However,therewillbesignificantnuancesandroomtomaneuverdependingontheexactstructureofadeploymentandtrainingprocedure.
10
/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-20
23-02-01/
11
/2022/08/01/david_holz_midjourney/
12
/new
13
https://blog.google/technology/ai/bard-google-ai-search-updates/
14Seediscussionsby,e.g.,
McCann
(
2021
);
Lemley&Casey
(
2020
);
Grimmelmann
(
2015
);
Sobel
(
2017
)formoreexaminationofmodelparametersandmodeltraining.
FoundationModelsandFairUseAPREPRINT
5
Transformativeness.Whentheoriginalworkistransformative,thisweighsheavilyinfavoroffairuse.Empiricalstudieshavefoundthatthetransformativenessfactortendstobemostdispositiveinlegalanalysesandisheavilyemphasizedinassessmentsoffairuse(
Asayetal
.,
2020
).Forexample,whenGooglecopiedpartsoftheJavaAPIforAndroid,theSupremeCourtfoundthatthiswasfairuse.Ittookintoaccountthattheamountofcodecopied(asmallpercentageofthederivativecodebase),andtheendproductwastransformative(
GoogleLLCv
.OracleAmericaInc.,
2021
).Similarly,GoogleBookscanshowportionsofbookstousersbecausethepercentageofthedisplayedbookissmallandtheusecaseistransformative(fromtheoriginaluseofreadingabookcover-to-covertoanewusecaseofsearchingquicklythroughabook)(
AuthorsGuild,Inc.v
.Google,Inc.,
2dCir.2015
).
ForscenariosconcerningmachinelearningandAI,somelegalscholarsbelievethatfairusecoversmosttypesofmodeltrainingwheretheresultingmodelfunctionsdifferentlythantheinputdata,particularlywhenthemodeltargetsadifferenteconomicmarket(
Lemley&Casey
,
2020
;
Carroll
,
2019
).Inpart,theseargumentssometimesanalogizetocasesrelatedtointermediatecopying—aslongasthe“defendant’sendproductwasatransformativenewworkandthecopyingwasanecessarysteptogetthere,”thecopyingofcopyrightedmaterialiscoveredbyfairuse(
Lemley&Casey
,
2020
).Forfoundationmodelsthatarenotappliedinagenerativecontext,thisargumentcanbeagoodfit.Forexample,trainingarecommendationsystemorsearchengineoncopyrightedbooksislikelysufficientlytransformativefromthepurposeoftheoriginalbookanditstargetmarkets,andisthuslikelytobeconsideredfairuse—asinGoogleBooks.
However,thestorymaybedifferentforgenerativeusecases.ForgenerativemodelslikeDALL-EorGPTthatproducecreativeoutputs,thesituationislesslikelytobeproblematiciftheoutputsdonotcopyasubstantialportionofanyexistingwo
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