2026投资领域大语言模型偏见治理报告：从检测到缓释

©2026CFAInstitute.Allrightsreserved.September2026

TECHNOLOGY

REPORT

ManagingLLMBias

inInvesting:From

DetectiontoMitigation

JamesTaitandBrianPisaneschi,CFA

ExecutiveSummary

TheuseofbiasedAI(artificialintelligence)systemsisbecomingapressingAIgovernanceissueaslargelanguagemodels(LLMs)areincreasinglyembeddedininvestmentmanagementworkflows.Financialauthorities,suchastheUSTreasury,havehighlightedAIbiasanditsconsequencesasakeyrisk.AIbiasescandistortinvestmentrecommendations,amplifyerrors,andexposefirmstoadversefinancial,regulatory,andreputationalconsequences.

ThisreportdiscusseshowbiasescanmanifestinLLMworkflows.Itis

relevantforanyinvestmentprofessionalworkingwithLLMsandusing

promptengineeringtechniques.Drawinganalogiestohumanbiases—whichcanbelooselycategorizedasimplicit(i.e.,unconscious)biasandexplicit

(i.e.,conscious)bias—weintroduceimplicitLLMbiasesandexplicitLLMbiases.ImplicitLLMbiasesarisefrommodelpre-trainingdataandarchitecture,whereasexplicitLLMbiasesemergethroughobservableLLM“decision”processeslike

toolusageanddataselection.WeshowhowbothhumanandLLMbiases

arepresentinaugmented(human+AI)workflows.Asaresult,itiscriticalto

becomeawareofanyimplicit,unconsciousbiasthatmaybepresentbefore

makingconscious,explicitdecisionsaboutwhatactions,ifany,shouldbetakentoreduceit.

Throughacontrolledcasestudy,weshowthatLLMsexhibitframing

bias—acognitivebiaswhereidenticalinformationleadstodifferentdecisionsdependingonhowtheinformationispresented.Theseverityofthisbias

dependsonthecontext.

Usingoursample,wefoundthatpromptsrelatedtodrawdownperformanceandclientrelationshipmanagementshowedthestrongestframingeffects.Wethenevaluatedseveralmitigationstrategiesthatattemptedtoreducethe

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ManagingLLMBiasinInvesting:FromDetectiontoMitigation

CONTENTS

ExecutiveSummarypg.1|Introductionpg.4|ButWhatIsBias?ClearingUpthe

Confusionpg.5|DisentanglingHumanandLLMBiasesinAugmentedWorkflowspg.11|

CaseStudy:Revealing,Quantifying,andMitigatingImplicitLLMBiasandImplicitHuman

Biaspg.15|RecommendationsandConclusionspg.27|Acknowledgmentspg.28|

AppendixA.AHypotheticalPortfoliofromOpenAI’sGPT-4opg.29|AppendixB.System-

LevelPromptUsedinLLMWorkflowpg.30|AppendixC.EvaluatingtheAbilityofGPT-5-NanoforFramingBiasDetectionpg.31|Referencespg.32

LLMframingbias.WefoundthatprovidingadefinitionoftheframingbiastotheLLMhadlittleeffectinreducingtheframingbias.Incontrast,ahuman-in-the-loopapproach—wherepromptsweremanuallyrestructured—providedthegreatestreductioninframingbias.Similarly,wefoundthata“bias-detection”LLMthatwasspecificallyinstructedtodetectframingbiaswas,inmostcases,abletodetectthebiasandrestructuretheprompt,significantlyreducing

theframingeffect—thoughthehuman-in-the-loopremainedthebestapproachoverall.

Fromtheseresults,weoutlinepracticalapproachesforinvestment

professionalstomitigateLLMbias.Theseincludestructuredprompt

engineeringandtheuseofLLM-basedguardrailsdesignedtodetectand

mitigatebias.Forexample,practitionerscandeploystructuredprompt

templatesthattaskaseparateLLMwithscreeninginputandoutputpromptsforseveralbehavioralbiasesbeforesuggestingcorrectiveactions.Amore

robustapproachincludesthedevelopmentofinternalbiasevaluationbenchmarksdesignedtosystematicallymeasureandscorebiasacrossvarioususecases.

Biascannotbefullyeliminated.However,througheducationand

experimentationaswellaspromptengineeringandcomparativetesting,practitionerscanbegintodetectimplicitLLMbiasesandmakeconscious,explicitdecisionsabouthowtomanageormitigatethem.Bydoingso,

practitionerscanmovefromunknowinglyintroducingimplicitbiastowardamoretransparent,explainable,andeffectivehuman+AIworkflow.

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ManagingLLMBiasinInvesting:FromDetectiontoMitigation

KeyTakeaways

OurfindingsrevealfourkeyfactorstoaddresswhentryingtomanageLLMbiasininvestingcontexts.

●Biasawareness

InvestmentprofessionalsshoulddistinguishbetweenimplicitLLMbiases—

whicharisefrommodelpre-training,architecture,andtrainingprocedures—

andexplicitLLMbiases,whichemergethroughobservabledecisionstheLLMmakes,suchasdataselectionandtoolusage.Humanbiasesinteractwithboth:InputpromptsandframingsteerLLMdecisionsandoutputs.Therefore,the

entirehuman+AIworkflowmustbeexamined,notjustthemodelinisolation.

●Biasdetection

LLMbiasesarerevealedthroughexperimentation.ImplicitLLMbiases

canbechallengingtoidentifyfromindividualresponsesandarebetterdetectedthroughcomparativetesting—thatis,bysystematicallyvaryingprompts,contexts,orinputsandthenmeasuringhowoutputschange.

Inourstudy,weshowhowidenticalinformationledtosignificantlydifferentLLMevaluationsduetodifferencesinthewaytheinformationwasframed(i.e.,theframingeffect).Theeffectwascontextdependent,withpromptsrelatedtodrawdownperformanceandclientretentionexhibitingthe

strongestframingeffectacrosstestedmodels.

●Biasmitigation

TellinganLLMtobeawareofabiasdidnotsignificantlyreducetheeffectofthebiasinourexperiments.Ahuman-in-the-loopapproach,wherepromptsweremanuallyaltered,workedbest.Aseparatebias-detectionLLMprovidedacompromisebetweenperformanceandspeedbyrapidlyscreeningand

restructuringbiasedprompts,thoughitdidnotoutperformthehuman-in-the-loop.ThisfindingsuggeststhatLLMscanbeusedforlarge-scalebiasdetection—thoughresultsshouldalwaysbeverified.

●Biasgovernance

Giventhatsomebiaswillalwaysremain,thegoalisnottoeliminatebias

buttomakeitmoredetectable,measurable,andmanageable.Thereisno

singletechniquethatcanbeusedtomitigatebias.Investmentprofessionalsshouldcombineeducationwithtask-specificexperimentation,systematic

promptengineering,checkpoints,andhumanoversightandexploreLLM-basedbiasdetection.Forhigh-riskandfrequent-usecases,firmscan

developtheirowninternalevaluationbenchmarks,testingnewmodelsastheyarereleased.Thesestepscanturnunconscious,implicitrisksintoexplicit,auditablechoiceswithinworkflows.

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ManagingLLMBiasinInvesting:FromDetectiontoMitigation

Introduction

Investmentprofessionalshaveafiduciaryresponsibilitytomakethebestfinancialdecisionsonbehalfoftheirclients.Thisdutycannotbefulfilledeffectivelyunlesspractitionersareawareofandmanagebiasesthatcanaffecttheinvestment

decision-makingprocess.InthecontextofAI,usingabiasedAIsystemisanethicalandfiduciaryconcern,withariskofdistortingrecommendations,amplifying

unfairdecisions,andexposingfirmsandprofessionalstofinancial,regulatory,andreputationalrisks.Therefore,inordertouseAIresponsiblyandethically,theabilitytodetect,measure,andmanagebiasinAIapplicationsiscrucial.

WhenLLMs,aformofAI,werefirstreleased,itwasquicklyapparentthattheyexhibitedbiases.Forexample,analysisbyBloombergrevealedthatOpenAI’sChatGPT-3.5discriminatedagainstcertainracesandgenderswhenrating

résumés(Yinetal.2024).ThisreflectedthelimitationsoftheunderlyingdatathatChatGPT-3.5wastrainedon—itwasgiveninsufficientdataonindividualsfromethnicminorities,therebyunderrepresentingthosegroups.Although

OpenAIdidnotdirectlycommentonthefindings,itadvisedrecruitersto“addtheirownsafeguardsagainstbias,likestrippingnamesoutofthescreening

process”(Bitter2024).Thoughnewermodelstendtoexhibitfewerbiases

thanoldermodelsowingtoimprovementsintrainingdataandguardrails,

biasesremain,andLLMdevelopersarecaughtinaperpetualstateofplayingcatch-up.Asnewmodelsarereleased,biasesareidentified,developersrefine,anewmodelisreleased—andthecyclerepeats.ThefieldofbiasdetectionandevaluationintheeraofLLMscontinuestoevolve,withevaluationbenchmarksbeingregularlyreleased.ThesebenchmarksattempttomeasuretheseverityofdifferenttypesofLLMbiasesacrossdifferenttasks(OpenAI2025).

AlthoughthenumberofstudiesidentifyingLLMbiasesininvesting-related

tasksisgrowing,agreatdealofconfusionremainsaroundhowthesebiasesareintroduced,theimpacttheymayhaveonfinancialandinvestmentdecisions,

andhowtheycanbepracticallymeasuredandmanaged.Forexample,anLLMmayappeartoexhibitanimplicitpreferenceforparticularstocks,sectors,

orassetclasseswhenprompted.Thispreferencearisesbecausesuchassets

aremorestronglyassociatedwithcertaincontextstheLLMhaslearned

duringitspre-training(e.g.,technologystocksbeinglinkedtohighgrowth

orstrongperformance).Asaresult,whenpromptsarealignedwiththese

contexts,suchasayounginvestorseekingnewgrowthinvestments,the

LLMmaysystematicallyfavorthoseassets.Thisfavoritismcanleadtothe

underweightingofneworcontradictoryinformation,resultinginsystematic

errorsinjudgmentwhenperformingsuchtasksasdataanalysis,research,

orinvestmentdecision-making(Leeetal.2025).Additionally,therearefewLLMbiasevaluationbenchmarksforreal-worldfinancialtasks,makingitchallengingforpractitionerstounderstandhowusingAIaspartoftheirworkflowsmay

introducebiasesandhowtheycantakepracticalstepstomitigatethem.

Inthisreport,weaimtoclarifyhowbiases—longstudiedinhumandecision-making—canmanifestinLLMandagenticAItasksinfinance.Westartoffby

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definingbiasesacrossthevariouscontextsinwhichtheyappear.Wethen

introduceLLMbiasesthatweanalogouslymaptohumanbiases,whichcan

beimplicitly(unconsciously)orexplicitly(consciously)introducedduring

LLMtasksorworkflows.Wethenexaminesomeoftheexisting,practical

approachestodetecting,quantifying,andmitigatingthesebiases,emphasizingtheroleofstructuredworkflows,education,andhumanoversight.Bydoing

so,weseektoraiseawarenessofhowbiasescanbeaddressedinthisageofhuman+AIintelligence,equippinginvestmentprofessionalswiththeconceptualframeworksandpracticaltoolsneededtoimproveethicalandresponsibleAIusage.

ButWhatIsBias?ClearingUptheConfusion

AccordingtotheCambridgeDictionary(2026),biasisdefinedas“theactionofsupportingoropposingaparticularpersonorthinginanunfairway,becauseofallowingpersonalopinionstoinfluenceyourjudgement.”Inlegallanguage,biasissimilarlydefinedas“judgementbasedonpreconceivednotionsorprejudices,asopposedtotheimpartialevaluationoffacts”(Hellströmetal.2020,p.2).

Thesedefinitionsprovideapictureofbiasasitmanifestsinthehumanbrain:

Itcanbethoughtofasasystematicdeviationfromsomenotionof“truth”or“idealjudgment”asaresultoferrorsinourthinkingprocesses.However,therearemanyotherdefinitionsofbias—forexample,statisticalbiasandAIbias.

Therefore,wemustfirstdefinethesebiasesbeforeintroducingLLMbiasesandhowtheyrelatetoexistingdefinitions.

CognitiveBias

Cognitivebiasesaredefinedas“unconsciousandsystematicerrorsinthinkingthatoccurwhenpeopleprocessandinterpretinformationintheirsurroundingsandinfluencetheirdecisionsandjudgments”(TverskyandKahneman1974).

Theycanbegroupedintotwocategories:beliefperseverancebiasesand

processingerrors.Anexampleofbeliefperseverancebiasisconfirmation

bias,whichoccurswhenindividualsseekinformationthatconfirmsrather

thanchallengestheirbeliefs.Examplesofprocessingerrorsareanchoringandadjustmentbias,whichoccurwhenindividualsrelyoninitialinformationtooheavily(anchoring)andfailtoadequatelyadjusttheiropinionawayfromit

(adjustment).

EmotionalBias

Emotionalbiasesrelatetotheemotionsandfeelingsofindividuals.Theystemfromimpulsesandintuitions,notfacts,andaremorechallengingtodetect.

Examplesofemotionalbiasesincludeloss-aversionbiasandoverconfidencebias.Loss-aversionbiasoccurswhenindividualsassignahighersignificancetoafixedmonetarylossthantothesamemonetarygain.Overconfidence

biasoccurswhenindividualsexhibitunwarrantedfaithintheirownreasoning,judgments,andcognitiveabilitiesthatisnotbackedbyobjectiveevidence.

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Implicit(Unconscious)Bias

Implicitbiasesareunconsciousbiasesthat“affectourperceptions,actions

anddecisions,”oftenleadingtounequaltreatmentofothers(ShahandBohlen2025).Examplesofimplicitbiasesarecognitivebiasesandmostemotional

biases,aswearenotconsciousoftheirexistence.

Explicit(Conscious)Bias

Explicitbiasesarebiasesthatanindividualisconsciouslyawareofandcanthereforeidentify,reflecton,andchallenge.Thesebiasesoftenoriginateasimplicit,unconsciousbiasesbutbecomeexplicitoncetheyarerecognizedthrougheitherself-reflectionorexternalfeedback.Forexample,aninvestormaybecomeawareofapersistentpreferenceforholdingaparticularassetintheirportfolio,evenwhenthatpositionisnolongerjustified.Many

unconsciousbiasescanberesponsibleforthisbehavior;however,oncetheinvestorrecognizesthebiasandcontinuestoholdtheasset,ithasbecomeanexplicitbias.

StatisticalBias

Instatisticsandmachinelearning,the“bias”ofanestimatorisstrictlydefined

asthedifferencebetweentheexpectedvalueofanestimator,E(),andthetrue

parametervalue,θ.Thisiswrittenmathematicallyasfollows:

bias()=E()_θ.

Wecouldbetryingtoestimateafunction,inwhichcasewouldrepresentthe

parametersofourfunction,suchasthebetacoefficientsinalinearregression.

Orwecouldbeestimatingadatadistribution,inwhichcasewouldrepresent

theparametersgoverningtheshapeofthedistribution,suchasthemeanandvarianceforaunivariatenormaldistribution.Statisticalbiasarisesfromdata

collectionanddataanalysis.Atthedatacollectionstage,biasisintroduced

whenthesampledatadonotsufficientlyrepresentthepopulationdata,

commonlyreferredtoasselectionbias.AsimpleexamplewouldbewantingtoestimatethemonthlyreturndistributionfortheUSstockmarketbyusingtheS&P500Indexconstituentsasthesample.Inthiscase,ourestimatorsofthe

populationdistributionwouldbesystematicallybiased,becausetheexclusionofsmall-capstocksfromthesamplemayreducethevarianceofthedistributionofreturns.Atthedataanalysisstage,biasarisesfrommodelselectionand

estimation.Forexample,usingalinearmodeltomodelanon-linearrelationshipbetweentwovariableswillresultinbiasedestimates.

TheBias–VarianceTrade-Off

Thebias–variancetrade-offdescribesthebalancebetweenamachine-learningmodel’scomplexityanditsabilitytogeneralizetootherdatasets.Amodelhaslowbiasifthedifferencesbetweentheexpectedestimates(predictions)and

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thetrue(real)valuesaresmall,whichoccurswhenthemodel’sapproximate

function,(x),resemblesthetruefunction,f(x).Amodelhaslowvarianceifit

hassimilarperformancesacrossdifferentdatasets.Increasingthecomplexityofamodelwillnormallyresultinlowerbias,asthemodelcanfitdatabettertoproducemoreaccuratepredictionsonaverage.However,thisincreasesthevarianceofitspredictionsonotherdatasets,resultinginpoorerperformance.Overfittingoccurswhenamodelhaslowbiasbuthighvariance;ithasan

excellentperformanceononedatasetbutgeneralizespoorlytoothers.Usingacommontargetanalogy,Exhibit1depictstherelationshipbetweenbias

andvariance.

Machine-LearningBias

Inthecontextofmachinelearning,biaswasfirstintroducedbyTomMitchell

(1980,p.1)tomean“anybasisforchoosingonegeneralizationoveranother,

otherthanstrictconsistencywiththeobservedtraininginstances.”Usingan

exampleofaclassificationproblem,hestatedthatanunbiasedlearner“makesnoaprioriassumptionsaboutwhichclassesofinstancesaremostlikely,but

basesallitschoicesontheobserveddata”(Mitchell1980,p.1).Inthisscenario,thereareaninfinitenumberoffunctions(calledthefunctionspace)thatcan

beusedtoperfectlymatchtheobserveddata(Hellströmetal.2020).Ifthis

soundslikeoverfitting,itis—mostofthesefunctionsmemorizetheobserveddataandwillnotgeneralizetounseendata.Theverynatureoftrainingamodeltoapproximateafunctionthatcangeneralizetounseendataintroducesbias.Thisisnotaflawbut,rather,afundamentalrequirementforanymodeltomakean“inductiveleap”togeneralizetounseendata.

AIBias

AIbiasiscommonlydescribedasthetendencyforAImodelstoproduceoutputsthatreflectharmfulbiasesintheirtrainingdata.Thisdefinitionisalsocommonlyusedtodescribemachine-learningoralgorithmicbias.

Theunderlyingdatacanbebiasedforavarietyofreasons.Forexample,

aclassificationmodeltrainedtoapproveloanapplicationsusingdatafrom

alargelyAmericanpopulationcanproducebiasedoutputsifdeployedoutside

Exhibit1.TheBiasandVarianceofaMachine-LearningModel

LOWVARIANCE,HIGHBIASHIGHVARIANCE,LOWBIASHIGHVARIANCE,HIGHBIASLOWVARIANCE,LOWBIASSource:ImagecreatedusingGoogle’sNanoBananaPromodelwithhumanedits.

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Consistentbutinaccurate

Scattered,

butcenteredonaverage

Consistentandaccurate

Notscatteredandcenteredonaverage

Inconsistentandinaccurate

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theUnitedStates,asthemodelwouldtendtoapproveAmericancustomersoverothernationalitiesthatareunderrepresentedintheoriginaldata.

LLMBias

LLMbiasdetectionandcategorizationisafast-movingfield.However,asLLMsarepre-trainedonmostlyhuman-generateddataandaredesignedtomimic

humanthoughtanddecision-making,inthisreportwedrawanalogiestohumanbehavioralbiases.WesimilarlycategorizeLLMbiasasimplicitorexplicit,basedonwhetherLLMresponsesaredueto“unconscious”or“conscious”decisions

itcanendorseandcommunicate.Tobeclear,LLMsarenotconsciouslyawareofanydecisiontheymake;theyonlyappearsothrough“reasoningtraces”thatreflecthumancognition.However,aswehavenotseenanydefinitionsthat

clearlydistinguishthewaysinwhichLLMbiasescanappearinthegrowing

prevalenceofaugmented(AI+human)intelligence,weofferthesehuman-

baseddefinitionsasastartingpoint.WethenprovideexamplesofbothimplicitandexplicitLLMbias.

Implicit(Unconscious)LLMBias

ImplicitLLMbiasisatypeofAIbias—itisasystematicdeviationinLLMoutputsresultingfrompre-trainingdata,modelarchitecture,andtrainingprocedures.AsLLMsaretrainedonvastcorpusesofhuman-generatedcontent,many

oftheseimplicitbiasescananalogouslybemappedtohumancognitiveandemotionalbiases.Forexample,whenpromptedtoproducestockportfolioswithoutadditionalcontext(suchasportfolioobjectives,clientrisktolerance,orfundamental/technicaldata),LLMshavebeenshowntoexhibitbiased

investmentrecommendations,1commonlysuggestinghigh-profileAmericanstockssuchasMicrosoft,Apple,Google,andJohnson&Johnson(Zhietal.2025).ChoosingthestocksthatappearmostofteninLLMs’pre-trainingdataindicatesavailabilitybias,acommoncognitivebiaswhereindividualsrelyoninformationthatiseasilyobtainabletomakedecisions(AppendixA).2

Additionally,ifanLLMhasimplicitpreferencesforparticularstocks(orasset

classes),itmayfailtoadequatelyupdateitsrecommendationinresponseto

newinformationprovidedbytheuser,indicatinganchoringbias.Thistypeof

anchoringbiashasbeendemonstratedinrecentresearchbyLeeetal.(2025),

whofoundthatLLMswithaninitialpositiveviewonastock(theyweremorelikelytoissuea“buy”recommendationintheabsenceofadditionalcontext)required

substantiallymorenegativeevidencetochangetheirrecommendationtoa“sell”comparedtostocksthatanLLMhadaninitiallyneutralopinionof(equallylikelytorecommendabuyorsellrecommendationwithoutadditionalcontext).

1Here,weusethedefinitionofinvestmentrecommendationgivenintheCFAInstitutereport“TheFinfluencer

Appeal:InvestingintheAgeofSocialMedia”(EspeuteandPreece2024):“Contentthatrecommendsaspecificcourseofactioninrelationtoaspecificinvestmentorproviderofaninvestmentproductorservice—forexample,apieceofcontentthatrecommendsbuyingorsellingafinancialproduct.”

2SeeAppendixAforasimpleexperimentinwhichOpenAI’sGPT-4owasaskedtoproduceastockportfolioandrecommendedAmericanlarge-capstocks,mostofwhichwerefromthetechnologysector.

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OtherimplicitbiasesuniquetoLLMsarebeginningtobeexplored.Forexample,LLMshavebeenshowntoexhibitpositionalbias—changingtheirresponses

basedontheorderoftheprovideddataintheinputprompt.Positionalbiasisatypeofframingbias—acognitivebiasthatoccurswhenthesameinformationispresentedindifferentways,leadingtodifferentdecisions.Forinstance,when

anLLMisaskedtochoosebetweentwostocks,theLLMmayfavorthestock

listedfirsteveniftheunderlyinginformationprovidedforeachstockisidentical(Diminoetal.2025).Likewise,whenprovidedwithalong-formunstructured

textdocument,LLMsaremorelikelytoextractcontentfromthebeginningandtheendofeachpassage,ignoringinformationinthemiddle(Bastianelloetal.

2024).Thisfindinghasimplicationsforsummarizingsuchcontentasearningscalltranscripts,whereimportantinformationinthemiddleofthetranscriptcanbemissed.Additionally,LLMshappentobemoreeffectiveatretrieving,parsing,andanalyzingcertainfiletypesthanothers(ImprovingAgents2025).ThisisanimplicitLLMbiaswhichcloselymirrorshumanavailabilitybias,asitcanleadtoanLLMprocessingeasier-to-retrieveinformationinsteadofmorerelevantbut

lessaccessibleinformationduetodifferencesinfilestructure.ThisisduetoanLLM’strainingdataandmodelarchitecture,asthemodelshavelikely“seen”moreMarkdownorHTMLfilesthan.csvfiles.

Lastly,LLMsareincreasinglyusedtoevaluatetheresponsesofotherLLMs—

knownas“LLM-as-a-judge.”However,somestudieshaveshownthatthese

evaluationapproachesaresubjecttoself-preferencebias:LLMsarelikelytoranktheirownoutputs“better”thanthoseofothermodels(Wataokaetal.2025).

SuchbiascannegativelyaffectLLMworkflows.Forexample,considerananalystusingtwoLLMsfromdifferentAIproviders(e.g.,OpenAI’sGPTmodeland

Google’sGeminimodel)togenerateopposingviewsforthesameinvestment.TheGPTmodelistaskedwithproducingabullishview,andtheGeminimodel

istaskedwithproducingabearishview.AthirdLLMcanbeusedtodeterminethefinaldecisionandweightheinformationinthebullishandbearishviews

accordingly.ButiftheLLMjudgeisfromthesamemodelfamily(i.e.,GPTor

Gemini),itmaybebiasedtowardtheoutputsofitsownmodelfamily,leadingtoasystematicbiasinissuingfinalinvestmentrecommendations.Similartohowindependentreviewersareusedtoreviewacademicpapers,anindependent

LLMfromanotherprovider(e.g.,Anthropic’sClaudeSonnet/Opusmodels)canbeusedasthejudgetolimittheriskofself-preferencebias.

AlltheseexamplesareofimplicitLLMbiases,astheLLMisunawareofits

impactwhenrespondingtoqueriesormakingdecisions.Wherepossible,

itisimportanttodiscoverwhereandhowtheseimplicitLLMbiasesmight

arisesoindividuals(andLLMs)canreflect,makingconsciousdecisions

abouttheimpactsofthesebiasesandwhetherandhowtomitigatethem.ThisapproachallowsorganizationstomoveimplicitLLMbiasfroman

unrecognizedrisktoamanageddesignchoice,wherebetterdecisionsaremadewithgreatertransparencyandaccountability.Exhibit2showsan

infographicsummarizingthesourcesofimplicitLLMbiasandhowitleadstobiasedoutputs.

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ManagingLLMBiasinInvesting:FromDetectiontoMitigation

Exhibit2.ImplicitLLMBias

Pre-training

AIbiasLLM

Fine-tuning

ImplicitLLMbias

Biasedoutput

Sources:Iconswereadaptedfromworkbythefollowingauthorson

F:

pojokd,Kharisma.

Explicit(Conscious)LLMBias

WedefineexplicitLLMbiasasabiasthatemergeswhenanLLMappearsto

justifyitsdecisions.ThisconsciousbiasoccursinsettingswhereanLLMhas

accesstootherdatasources,suchasexternaltools,documents,orweb-searchcapabilities.Insuchcases,theLLMdetermineswhichsourcestoconsult,whichfunctionstoinvoke,whichwebsitesanddatatouse,andsoon.Thesedecisionscanbetrackedthroughreasoningtracesorlogs.Anexamplewouldbeusing

anagenticAIworkflowtoconductpublicequityanalysis,wheretheLLMhas

accesstosuchdataasnewsarticles,earningscalltranscripts,financialreports,andanalystrecommendations.Observingtheworkflowandthedatathatthe

LLMpullsandusestoconductitsanalysisgivesvaluableinsightintoits“explicit”decisionsandwhethertheresultingoutputwillbebiased.Likehumanactions,LLMactions