beyond average tools. on the use of ‘dumb’ computation and ... · beyond average tools. on the...

Running with Scissors, 13th EAD Conference University of Dundee, 10-12 April 2019

Copyright © 2019. The copyright of each paper in this conference proceedings is the property of the author(s). Permission is granted to reproduce copies of these works for purposes relevant to the above conference, provided that the author(s), source and copyright notice are included on each copy. For other uses please contact the author(s).

BeyondAverageTools.Ontheuseof‘dumb’computationandpurposefulambiguitytoenhancethecreativeprocess PhilippaMothersilla&V.MichaelBoveaaMIT Media Lab, USA *Corresponding author e-mail: pip@mit.edu

Abstract: In theearly phasesof thedesignprocess, embracing chance intrusions,seemingirrelevanceandambiguitycanleadtoconsideringconceptsindifferentwaysandprovokenewideas.However,thecomputationaltoolsweareincreasinglyusingin thesephasesvalueefficiencyoverserendipity; technologieswhose foundationsareanaverage.Thispaperpresentsa‘BeyondAverage’approachthatwasusedtodeveloptwotoolsthatuse‘dumb’computationandpurposefulambiguitytoenhancethecreationofnovelideas.Resultsfromstudiesusingthetoolsinadesigntaskshowthatcomputationaltoolswithamediumlevelofcontextualityandahigherlevelofinterpretabilitycanpositivelyinfluencethecreationofnewideas.Discussionsabouttheroleofcomputationintheearlyphasesofthedesignprocesssuggestthattoolswithhigherlevelsofcreativeagencycancontributetothedesigner’screativeagencyandbecomeamorenaturalpartnerintheseactivities.

Keywords:Computationaldesigntools;artificialintelligence;creativity

1.IntroductionRenowneddesignerKenyaHara(2007)writesthat“creativityistodiscoveraquestionthathasneverbeenasked”.Thisisespeciallytrueintheearlyphasesofthedesignprocess—thoseofdiscoveryanddefining—whereexploringnewinformationandconsideringitinnew,non-obviouswayshelpsdesignerstorevealnewmeaningsandassociations(Mendel,2012).Particularlyintheseearlyexplorations,usingdesigntoolsthatembracelessliteralanalogiesandallowforambiguityandserendipity(Gaver&Dunne,1999;Mothersill&Bove,2017)canprovokenewideasthatcrossovertheboundariesbetweenexistingconceptualschemas(Gero&Maher,2013).Thesecreativeleapscanhelpdesignersbreakthroughtothatmomentofinspiration(Cross,1997)whichguidesthedevelopmentofthedesigninthelatterphases.

Computationisincreasinglybeingintegratedintothetoolsusedthroughoutthecreativeprocess.Whilecurrentlybettersuitedtothemorewell-boundeddeductiveprocessofthelatterphasesofthedesignprocess(Bernal,Haymaker&Eastman,2015),ComputerAidedDesign(CAD)toolsarestartingtobeusedintheseearlier,moreabstractexplorations.Technologiessuchasgeneticalgorithmsandmachinelearningprogramsusestatisticalmathematicstorepeatedlygenerate,evaluateandoptimisedesignsolutions(Sjoberg,Beorkrem&Ellinger,2017),aswellasnavigateusthroughthe

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multitudeofonlinecontentthatcaninspireournewcreations.Attheverycoreoftheseintelligenttechnologiesisanequationcalledthe‘costfunction’;theaverageoftheerrorbetweentheexpectedandactualdata,calculatedoverandoveragain.Itisfromminimisingthisaveragethatwearequicklyguidedtoconvergeonafewspecific,quantitativelybettersolutions,butisthisthebestapproachfortoolsusedintheearlier,moreabstractexplorationsofthedesignprocess?

Thesecomputationaltoolsareundoubtedlybetterthanhumansatquicklygeneratingamultitudeofdifferentdesignoptions(Steinfeld,2017),butwhenitcomestodiscoveringtheradicalinspirationneededforcreativebreakthroughsthesetechnologieshavetheirlimitations.The‘intelligent’toolsweareincreasinglyusingtofindinspirationforournewdesigns,suchasGoogleandPinterest,donotalwaysprovidethediversityofinformationandimagesthatweneedtoguideourresearchintheearlyphases;informationthathelpspromptustoquestionconceptsindifferentways,revealnewinsightsorinspireunexpectedideas(FultonSuri,2008).Artificialintelligencecanindeedhelpusfindhugeamountsofdataveryquickly,butifwearenotcarefulthesetechnologiescanalsopullusdownverycreativelyproblematic,average-driven,algorithmicrabbitholes(Carter&Nielsen,2017).

Perhapswedon’talwaysneedtheseintelligenttoolstobethat‘smart’orprovideuswithsuchoptimised,unambiguousresponses.Theambiguityprovidedbyimperfecttechnologiesandrandomnessdeliveredby‘dumb’AIscanactuallyaugmentourhumansmartness,andpotentiallyevenourcreativity(Shirado&Christakis,2017;Mothersill&Bove,2018).Thispaperexploresthisseemingparadoxandasks:howcandesigntoolsthatuse‘dumb’computationandpurposefulambiguityinfluencethecreativeprocessintheearlyphases?

2.ThelimitationsofaverageWhatisthebestwaytoLarissa?ThisisthequestionthatPlatoimaginedhisteacherSocratesandtheGreekgeneralMenodiscussing(Plato).SinceMenowasborninLarissa,heknewverywellhowtogettherefromprevioustravels.Aninexperiencedtravelercouldalsouseamaptomakethejourneymostefficient.Or,asatourist,hemightwishtoseethesitesalongthewayandthereforetakealessdirect,butpotentiallymoresatisfyingroute.Themoreadventuroussoulmightjustheadoutinthegeneraldirectionandletchanceguideheractionsalongthejourney.Thecoreofthisdialogueistoquestionwhatknowledgeis,butitalsorelatestoanimportantconsiderationforanyresearchintodevelopingnewcomputationaldesigntools:howshouldtheyguideus?Thisquestionhasbeenconsideredextensivelyinthefieldofcyberneticsandprovidesusefulinsightsintothechallengesforintegratingautomatedcomputationintothedesignprocess(Dubberly&Pangaro,2015).

CyberneticscomesfromtheGreekwordkybernētēs(κυβερνήτης)meaning"tosteer,navigateorgovern".Atitsmostbasic,acyberneticapproachtakesfeedbackfromasystemtounderstandhowtoreachagoalinthemostefficientway.BuildingonPlato’sanalogy,asacrowflyingoverthemountainsofAthens,wecouldnavigateourwaytoLarissausingcompassbearingsalongthemostdirectroute,modifyingourmovementstogettoourendgoal.Orappliedtothedesignprocess,computationalsystemsthatusetheseapproachescanhelpusanswerquestionssuchas“whatpossiblesolutionsfitthesegoals&constraints?”(Case,2018).

Ourcomputationaldesigntoolsareincreasinglyrelyingontheseintelligentstatistically-drivenapproachesor‘technologiesoftheaverage’.Byoptimisingtheaverageatthecoreofthecostfunctiondescribedabovetoquicklyconvergeonafewspecific,quantitativelybetter‘answers’,computationaltoolssuchasgeneticalgorithmsandmachinelearningprogramscanhelpusquicklydiagnoseamedicalcondition(Mukherjee,2017),generatethousandsofdesignsforachair(Rhodes,2016),orcreatea‘new’workofartbyanoldMasterpainter(Korsten,2016).

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Whilethesetechnologiescanhelpusfindhugeamountsofcontentinsearchenginesorquicklygeneratedesignsfromsetsofdata,theefficiency-basedapproachtoanalysinginformationusedbythesesystemsmeansweareonlypresentedwiththeaverageofthismaterial.Googling‘chair’maynotbringyouimagestoinspirenewideas;youmightjustgetacollectionofpicturesthatlooksimilar.Pinterestboardsareoftenbecomingcollectionsofhomogeneouslysleekdesigns;somuchsothatdesignerssuggestthatwehavereachedthe“Pinterestsingularity”andareshunningitinanattempttonotcreateaverage-lookingdesigns(Gong,2018).

Integratingthenotionoftheaverageintothedesignprocessisnotnew(Rose,2016):fromitsoriginalapplicationtounderstandthediversityinhumansizes(leadingtotheBodyMassIndex),toitsuseinthefieldofscientificmanagement(orTaylorism)tooperationalizetheprocessesoffactoryworkers,tointegratingitintostandardizedergonomicmeasurementstodesignmass-consumableobjects(Dreyfuss&Dreyfuss,1967).Butjustasitsapplicabilitywasquestionedwhenitwasdiscoveredthatnoneofover4000pilotsmatchedallofthe10averagebodydimensionsthatcockpitswerebeingdesignedfor(Daniels,1952),perhapsweshouldbequestioningthesuitabilityoftechnologiesthatrelyonanefficiencyapproachusedintheearlyphasesofthecreativeprocess.

Incomparisontothiscurrentcomputationalapproachthatprioritisesefficiency,theearlyphasesofthedesignprocessneedalesslogicalexplorationfullofexperimentsandquestions(Schön,1983);wearetheadventurerswhoprefertherichnessofthescenicroutetoLarissa!Especiallywhendealingwiththeoftenill-formulated‘wickedproblems’thatwearedesigningfortoday(Churchman,1967),thebeginningofthedesignprocessfeelslikeaimingatashiftingtargetwhereweoftendon’tfullyunderstandtheproblem,letalonehaveadefinedgoal(Rittel,1988).Appreciatingthisflexibilityintheearlyphasesofthedesignprocessisveryimportantbecause,justas“weshapeourtoolsand,thereafter,ourtoolsshapeus”(Culkin,1967),theinspirationwecanobtaintoguideourdesignsisbeingshapedbythealgorithmsthatrulethemachinesweusetosearchfornewideas(Lynch,2016).Theargumentforintegratingtheseefficiency-basedapproachesintoourdesigntoolsisoneofconvenience(Carter&Nielsen,2017).Butcanoutsourcingourcreativetaskstotheseoverly‘user-friendly’interfacescontributetocognitiveinertia?Whilepartofthecreativeprocesscanindeedbenefitfromthecompetenceandefficiencythattheseintelligenttoolscanprovide(Steinfeld,2017),radicalbreakthroughscomeonlyfromconsideringconceptsmoreabstractly(FultonSuri,2008)andchallengingtheexistingprinciplesinourfields(Nielsen,2016).

3.AlternativestotheaverageItisoftenintheearlyphasesofthedesignprocess—thoseofdiscoveryanddefining—thatcreativeleapscanleadtoradicalbreakthroughs(Cross,1997).Activitiesinthesephasesinclude‘gatheringdisparateinformation’,‘generatinghypotheses’and‘identifyingnoveldirections’(Mothersill&Bove,2018);activitieswhereawidevarietyofinformationisexploredandconsideredinnon-obviouswaystohopefullyrevealnewmeaningsandassociations.Theseactivitiesinvolvetheoftenserendipitouscreativechallengesthathumansareverygoodat:consideringdifferentcontexts,embracingambiguityandusinganalogytofindnewinterpretationsandassociations(Bernaletal.,2015).

TheseelementsofthecreativeprocesswerechampionedbycreativityresearchersEdwarddeBonoandWilliamGordon.DeBonodevelopedthepracticeoflateralthinking,whichutilisedthefactthatthehumanmindisveryefficientatrecognisingpatterns;ifwearepresentedwithinformationwhichdoesnotimmediatelyseemrelevant,wenaturallytryto‘makesense’ofit.Lateralthinkingwelcomeschanceintrusions,irrelevance,andambiguityinordertoprovokedifferentpatternsandcreatenewideas(Bono,1970).ThisstrategywasalsoembracedbyGordoninthepracticeof

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synectics—literallymeaning‘thejoiningtogetherofdifferentandapparentlyirrelevantelements’—where‘perfect’ideasarerejectedinfavourofthenon-rationalitythatcangeneratemoreevocativemetaphorsandseedsofinspiration(Gordon,1961).

Whencomparedtothecertaintyofferedtousthroughthetechnologiesoftheaveragedescribedabove,theearlyphasesofthedesignprocessoftenfollowalesslogicalandpredictablepath(Mitchell,1993)andsopotentiallyrequiredifferentapproaches.Purposelyintegratingnoiseintotheverypredictableandcontrollablesystemswearesofamiliarwith,suchasthroughambiguityandchanceintrusions,can“createamarginoferrorinwhichcreativeinterpretationandmisinterpretationmightthrive”(Bernes,2017).Ifweareopentoexploringthesemomentsofcreativereinterpretation,wemightdiscoverentirelynewapproachestoadesignproblemandinvent“waysofthinkingwhichhaven'tyetbeeninvented”(Nielsen,2016).

Ifambiguityandopennesstochanceinterventionsareimportantaspectsoftheearlyphasesofthedesignprocessthatcanhelpusdiscovernewideas,thenwebelievetheyshouldalsobeintegratedintothetoolsweuseinthosedesignactivities.Incontrasttothedriveforquantification,optimisationand‘intelligence’incurrenttechnologies(Sjobergetal.,2017),weareexploringhowthemoreserendipitousprinciplesofcreativity—thoseofseemingirrelevanceandambiguity—canbeusedasanapproachforcreatingnewcomputationaltools.Thefollowingsectionsdescribethe‘BeyondAverage’approachwehavetakentodeveloptwocomputationaldesigntoolsandtheevaluationscarriedouttounderstandhowtheycanbeusedtogeneratenewideas.

4.A‘BeyondAverage’approach Buildingontheseserendipitousprinciplesofcreativitypresentintheearlyphasesofthedesignprocess,weproposethefollowingdesignspacedimensionstoguidethedevelopmentofcomputationaltoolsthatcancontributetotheactivitieswherenewideasarediscovered:

Contextuality Thisdimensionassessestheamountofcontextualinformation—orseemingirrelevance—thatthetoolusestoguidethecollection,generationandreviewingofinspirationalinformationanddesignoutputs.Thisdimensioncanalsorelatetothe‘smartness’ofthetool.Atoolwithahighcontextualityintegratesalotofadvancedcomputationsuchasthemachinelearninganalysisofextensivedatasetstocalculateacontextually‘optimised’andrelevantresponse,e.g.asusedinasearchenginesuchasGoogle.Incontrast,atoolwithlowcontextualityisonethatusesmuchsimpleralgorithmssuchasrandomness,hencedoesn’tgeneraterecommendationslearnedfromprevioususesandcanoftenprovideseeminglyirrelevantresponses.

InterpretabilityThisdimensiondetermineshowdirectorambiguoustheinformationorcreativeguidanceprovidedbythetoolis;isitaprescriptionoraprovocation?Thisdimensioncanalsorelatetotheagencythattheuserhaswhenusingthetool.ExamplesoftoolswithlowinterpretabilityaresearchengineslikeGooglewhereauserentersaspecificrequestandthetoolreturnsverydirectlyrelatedinformationthatrequireslittleadditionalinterpretation;theuserisveryactiveinchoosingaspecificconcepttoexplorebutmorepassivewheninterpretingtheinformation.AnexampleofatoolwithahigherlevelofinterpretabilityisEnoandSchmidt’s(1975)ObliqueStrategiescarddeckthatdoesnotrequiretheusertochooseaninitialconceptbutreliesontheiractiveperceptionandimaginationto‘makesense’ofthemoreambiguousinformation.

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Thesedimensionscreateaframingthroughwhichtoconsiderhowcomputationaldesigntoolscaninfluencethecreationofnewideasintheearlyphasesofthedesignprocess.Figure1showsourproposedpositioningofthe‘BeyondAverage’tools(describedinthenextsection)onthedesignspacedimensions,withGoogleincludedasabenchmarkofcurrenttools.

Figure1.Existingand‘BeyondAverage’toolsproposedmappingontodesignspacedimensions

5.‘BeyondAverage’designtools5.1.design(human)designcreativeprompttooldesign(human)designisacomputationalcreativeprompttoolthatprovokesnewassociationsbetweenconceptsinauser’sproject(http://reframe.media.mit.edu).Usingtextfromadesigner’sownnotesandreadings,design(human)designpresentsarandomisedprompt,helpingtojuxtaposeconceptsinnewways(Figure2).ThistoolwasdevelopedinresponsetofindingsfromfieldresearchatdesignconsultancyIDEO;thattoolsoffering‘structuredserendipitousinspiration’couldhelpprovokenewinterpretationsandideas(Mothersill&Bove,2017).

AsshowninFigure1,weproposethatthedesign(human)designtoolhasmediuminterpretabilityand,atitssimpleststate,alow-to-mediumlevelofcontextuality.Ifthetextcorpusismodifiedtoincludeinformationonlyrelatedtoacertaintopicorpersonaldataset,thelevelofcontextualitybecomesmedium-to-high.

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Figure2.Screenshotfromdesign(human)designcreativeprompttool

5.2.LookingSidewaysinspirationexplorationtoolLookingSideways(http://sideways.media.mit.edu)isanonlineexplorationtoolthatseekstoprovokeunexpectedinspirationandcreatenewassociationsbyprovidinguserswithaselectionofsemi-randomlychosen,looselyrelated,diverseonlinesourcesfromart,design,historyandliteratureforeverysearchquery(Figure3).

AsshowninFigure1,weproposethattheLookingSidewaystoolhasalowerlevelofinterpretabilitythanthedesign(human)designtoolduetotheuser’smoreactiveengagementwithit.Atitsmostsimplestate,ithasamediumlevelofcontextuality,howeverifthedatabasesthatthetoolissearchingarecustomisedtoacertaintopicorpersonal‘creativewateringholes’,thelevelofcontextualitycanbecomequitehigh.

Figure3.ScreenshotfromLookingSidewaysinspirationexplorationtool

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6.EvaluationmethodologyToevaluatethecreativepotentialofthesetools,wecarriedoutstudieswithbothprofessionalandstudentdesigners.18participants(10men,8women)tookpartinanobservedstudywheretheywereaskedtogeneratecreativeresponsestooneoftwothemes(“automatedsystems(inthehome,work,cityetc.)thatwetrust”and“thefutureofwellness(inthehome,work,cityetc.)thatisintegrated”)usingtheBeyondAveragetoolstoprovideinspiration.Thetextcorpusthatthedesign(human)designtooldrewfromwascustomizedforeachthemeusingwordsfromrelevantWikipediapagesandarticles.Theresultspages(includingimages,news,shoppingetc.)fromGoogle’ssearchenginewasusedasacontroltool.Theparticipantshad10minutestouseeachtooltoexplorethethemesandgenerateideasbasedontheinspirationtheyprovided,notingdownanyideasorsketchesusingpenandpaper.Aslearningfromprevioustoolswasinevitable,theorderofthetoolswasrandomisedacrossparticipants.Finally,participantscompletedasurveythataskedquestionsrelatedtothepotentialofeachtooltoprovideunexpectedcreativity(https://bit.ly/2FkvMEU).

Shah&VargasHernandez’s(2003)metricsformeasuringideationeffectiveness—novelty,variety,quality,quantity—aswellasmetricsrelatingtodeBono’s(1970)analysisoflateralthinking—whetherideasareofimmediateusefulness,areasforfurtherexplorationornewapproachestoproblem,andiftheyareverticallyorlaterallyrelated—wereintegratedintoquestionsthatparticipantsratedona5pointLikertscale.Overallcommentsabouthowthetoolsinfluencedtheparticipants’generationofnewideas,howthetoolscouldintegrateintotheircreativepracticeandanysuggestionsformodificationswerealsocollected.

7.FindingsWhilewedidcollectnumericaldataaboutthecreativitymetricsanddesignspacedimensionsdescribedabove,weacknowledgethatitishardtodrawgeneralisablequantitativefindingsfromthesetypesofsubjective,noteasilyrepeatablecreativeinterventions,especiallywithourrelativelysmallsamplesize.Therefore,herewewillpresentgeneraltrendsindicatedbythequantitativedataandextendtheanalysisoftheseinsightswiththequalitativefeedbackalsocollected.Asbothofthethemestestedprovidedsimilarresponses(mostratingswerewithinoneLikertpoint),wehavecombinedthedataintoasingleaverageusedintheresultsbelow.Theredidappeartobesomeeffectsduetothedifferentorderofthetoolsshowntotheparticipants,butthosewillbediscussedfurtherinthenextsection.

Tounderstandiftheparticipantshadasimilarexperienceusingthetoolsasweexpected,Figure4showstheparticipantsratingsofhowcontextualandambiguoustheyconsideredresponsesgeneratedbythetools(Lowcontextuality/interpretability=1;highcontextuality/interpretability=5).Theparticipantsgenerallyagreedwithourhypothesisforwherethesetoolssitwithinthedesignspacedimensions:Googlewasconsideredtogiveverydirect,highlycontextualresponses,design(human)designwasconsideredtohavethemostinterpretabilityandmediumcontextuality,andLookingSidewayswasconsideredtogivemediumlyambiguousandcontextualresponses(slightlylowerthanourexpectation,likelyduetotechnicallimitationswiththeprototype).

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Figure4.Existingand‘BeyondAverage’toolsmappingontodesignspacedimensionsbyparticipantscomparedwithproposedmapping

Reviewingthedatamappedagainstthedesignspacedimensionsindividuallyrevealssomelargertrendsabouthowthelevelsofcontextualityandinterpretabilityaffectcreativeoutput.Figures5and6showtheratingsforeachofthetoolsforthemetricsdescribedabovemappedalongthedesignspacedimensions.Lineshavebeenaddedbetweenthediscretedatapointstoindicatetrendsinhowthecreativitymetricsmightvaryasadesigntoolincludesmoreorlesscontextualityandinterpretability.Quantityofideasisnotincludedasalltoolsgeneratedsimilarresults(1-2ideas),probablyduetotheshorttimeallowedforthetask.

7.1.TheinfluenceofcontextualityonthecreativeprocessFigure5showsthatGoogle—thetoolwiththehighestcontextuality—hadthelowestratingsformostofthemetrics(between2.33and3.83).Despiteparticipants’familiaritywithusingGoogletogatheralargequantityofinformationonatheme,itshighcontextualitymeantthisknowledgewassituatedintermsofwhatotherpeoplehavedoneandthoughtbefore;the“generallyaccepted‘norm’answers”.Whilethishelpedsomeparticipantsidentifycommonfeaturesortrends,itledotherstofeeltherewas“toomuchpriminginthewrongdirection.”ThehighcontextualityofGooglewasconsideredbeneficialwhentheparticipanthasalready“honedinonsomethingnarrow”andis“thinkingaboutframingtheirenquiry”,butwas“notusefulfordeeplyassessingwhere[their]ideasweresituated”andthereforenottherighttoolforcomingupwithnewideas.

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Figure5.Mapofcreativitymetricsagainstthelevelofcontextualityineachofthetoolsstudied

Incontrast,thedesign(human)designtool(mediumcontextuality)wasratedhighestforallmetrics(between3.17and4.67).Thelowerlevelofcontextualitywasfoundhelpfulinliberatingtheparticipantsfromtheirownpreconceptions.Beingprimedwithtextrelatedtothetwothemesallowedthetooltoeasilyprovidemanysimplebutdifferent“relativelystablestartingpoints”fromwhichideascouldbeconstructed.However,duetotheformatofthetool,someparticipantsfeltthatthepromptsoftenfellintomoreproject-basedtasksratherthangeneralinspiringconcepts,limitingtheirboundariesofthought.Anotherparticipantalsocommentedthatwhile“arbitrarinesscanbeverypowerfulforlateralthinking…sometimesitcanfeelforcedordifficulttodrawconnections”andthat“knowingwhentoskipandwhentoponder”aseeminglyirrelevantconnectionrequiresconsideration,andpotentiallyguidance.

HelpingtoseelinksbetweenideaswasoneofthefeaturesthatparticipantslikedintheLookingSidewaysexplorationtool;addingalevelofcontextualitytoseeminglyunconnectedconcepts.Thisabilitytovisuallymaphowrandomconceptsintersect“providednicetangents”toopenuptheirexistingideadomain.Asparticipantscontrolledthecontextoftheexplorationbyenteringtheirownsearchterms“someconnectiontothegoalisthere”whichguidedoneparticipant“intoaheadspacethatiscomfortableandthatIfeelauthoritativein,butisnewterritory.”Despitethisfeedback,participantsstillratedthetoolasfairlylowcontextualityanditdidnotscoreashighlyasthedesign(human)designtoolintermsofcreativity(between2.56and4.11).Ingeneral,participantslikedthatthesearchresultswerenotdefinedbypopularitysuchasonGoogle,butduetolimitationsinthenumberofcontentsourcesinthecurrentprototype,therewasn’talargeenoughamountofinformationavailabletoexploreaconceptdeeply—asGoogleprovides—orconsidermanynewperspectives—asthedesign(human)designtoolprovides.

Overall,itappearsthattoolswhichprovidemorehighlycontextualresponses,i.e.Google,aregoodforexploringanarrowsubjectoncedesignparameters(orsearchterms)areknownbutthefocusedrangeofsimilarinformationlimitstheabilitytogeneratenewideasorconnections.Toolsthathavealowercontextuality—design(human)designandLookingSideways—canprovidetangentiallyassociatedresponsesthatpromptparticipantstoreconsiderhowconceptscouldbeinterpretedandconnected,providingthemwithinteresting“startingpoints”fornewideastoexplorefurther.

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7.2.TheinfluenceofinterpretabilityonthecreativeprocessMappingthesameresultsontotheinterpretabilityaxis,Figure6showsacleartrendtowardsgreatercreativitywithhigherlevelsofinterpretability.ForGoogle(lowinterpretability)participantsarereliedupontocomeupwithinterestingsearchterms,hencetheresponsescanonlybe“ascreativeasyourownmindessentiallyallowsyoutobe.”ThisimprovedwithhigherlevelsofinterpretabilityintheLookingSidewaystoolasitsabilitytoconnectrandomuser-definedconceptsprovidedfresh,unexpectedinputthat“encouragedmomentumandoutgrowth”and“awaytoriffoutfromwhereIalreadyam”.Presentingtheresponsesinamorevisual,unorganisedmanneralsoallowedfortheparticipantsto“makeamess”,inspiringlessliteralconnectionsandmorevariedinterpretationsbecausetheycanfindtheirownsenseinthecontent.

Thetoolthatprovidedthemostvariedandnewconnectionswasthedesign(human)designtool(highinterpretability).Participantsfoundthatwhentheyallowedthemselvestoletgoofcontrollingthetoolandconsidertheoftenambiguousresponsesinamoreflexibleway,therandomjuxtapositionsofconceptschallengedthemtotakeon“amorenon-structuralthinking”thatprompted“newandverydifferentpointsofviewsonmyideas”;afeelingthatseveralparticipantsdescribedasbeingrareincomparisontoothercompuationaldesigntoolstoday.However,whilemanyparticipantsenjoyedthepossibilitytoquicklyiteratethroughahighnumberofambiguouspromptsasithelpedthemgetintoadifferentmindset,afewconsideredthejuxtapositionofeventwooftheoftenverybroadconceptsrequiredalotoftimetothinkdeeplyaboutthepotentialconnectionsbetweenthem.

Overall,thereseemstobeacleartrendthathigherlevelsofambiguityintheresponsesprovidedbythetools—somethingwecouldalsodescribeasahigherlevelofcreativeagencyonthemachine’spart—allowedformorevarietyofinterpretationswithintheinformationpresentedandthereforeagreaterpossibilityfornewconnectionsandideastobemade.

Figure6.Mapofcreativitymetricsagainstthelevelofinterpretabilityineachofthetoolsstudied

7.3.TherolesoftheBeyondAveragetoolsinthedesignprocessFromtheresultsdiscussedabove,wesuggestthatcomputationaltoolswithamediumlevelofcontextualityandamedium-to-highlevelofinterpretabilitycanpositivelyinfluencecreativityintheearlyphasesofthedesignprocess.Thelateralresponsestosearchqueriesandsomewhatrandom

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provocationsenabledbyhigherlevelsofinterpretabilityallowparticipantstohavesomeagencyoverthedirectionofexplorationsbutalsobeprovokedtorethinkhowsomethingseeminglyirrelevantcouldbecontextual;responsesthatmakejustenoughsenseandprovideahighpotentialcontextualityforparticipantstogeneraterelevantbutnovelideas.

Figure7showsthisquadrantofthedesignspacedimensionswasalsoratedthemostdesirableforinspiringnewideas,supportedbythedesign(human)designtoolbeingratedfavouritebymostparticipants(11outof18).However,oneparticipantcommentedthatdesiringtoolsinthisquadrantofthedesignspaceseemedlikeaparadox.ThisrelatestohowparticipantsfeltGoogle—andthegeneraltrendforefficientsearchtools—hadconditionedthemtothinkinalogicalwayandusingtheBeyondAveragetoolshelpedthemembracemoreambiguous,non-deterministicapproaches.

Figure7.Proposedmappingofexistingand‘BeyondAverage’toolsontodesignspacedimensionscomparedwithdesiredamountofcontextualityandambiguityratedbyparticipants

Theeffectofthesedifferentapproacheswasnoticeablethroughtheordereffectsthatemerged.WhentheBeyondAveragetoolsweretestedfirst,participantsstartedtoconsiderhowtheycoulduseGooglemorecreatively,withmixedsuccessduetoitsmoreefficiency-orientedsearchapproach.

Thefactthatthesetoolscaninfluenceeachotherisanexcitingfinding.Whilesomeparticipantsdiddistinguishthetoolsforseparatedesignactivities,e.g.design(human)designforbrainstormingandLookingSidewaysasamappingtooltodocumenttheircreativeprocess,mostthoughttheywouldbeusefulasasuite.Usingamediumlycontextualisedversionofthedesign(human)designtoolwasconsideredausefulcreative‘icebreaker’forseedinginterestingnewdirectionsforfurtherexploration,followedbytheLookingSidewaystooltosuggestlateralconnectionsbetweenconceptsandGoogletogathermorefocusedinformationtofurtherframetheirideas.IntegratinginformationrelatedtokeyconceptsexploredinGoogleandtheLookingSidewaystoolbackintoamorecontextualisedversionofthedesign(human)designtoolwassuggestedasawaytofurthergeneratenovelbutmorefocusedideasrelatedtotheparticipant’semergingthemesanddesignparameters.

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Thisimaginedroleofthetoolsinthedesignprocessindicatesasomewhatcyclicalneedforhighlevelsofcontextualityandinterpretabilityinexplorationandideationactivities.Whenusingcomputationaltoolswithveryhighlevelsofcontextuality,e.g.Google,thecreativeagencyisdeterminedbythehuman;thesearchtermsaredeterminedbythedesigner,oftenthroughsomenon-computationalmeanssuchasbrainstorming.Whenthecomputationaltoolcanhavecreativeagencyaswell,e.g.throughusinghigherlevelsofinterpretabilityasthedesign(human)designandLookingSidewaystoolsdo,thecomputercancontributetothedesigner’screativeagencyandbecomemoreofanaturalpartnertoguidetheearlyphasesofthedesignprocess.

7.4.FutureresearchTheseresultshavehighlightedexcitingopportunitiesforustopursue.Modificationstothetoolsinclude:automatingthecustomisationofthetextcorpusinthedesign(human)designtooltogeneratemorecontextuallyspecificprovocations,expandingthenumberofcontentsourcesintheLookingSidewaystool,andfixingseveraluserinteractionissues.ExtendingtheLookingSidewaystool,wearealsodevelopingtheDesignDaydreamstableandpost-itnote;alow-techaugmentedrealitytoolthatcanprojectthedigitalcontentexploredontoobjectsintherealworld(Figure8).

Figure8.DesignDaydreamsaugmentedrealityviewers(aspartofalargeraugmenteddraftingtable)

Acknowledgingthatobservedstudiesarelimitedwheninvestigatingthedesignprocess,wearealsocarryingoutlongerunobservedstudiestofurtheranalysethetools.Intheselessstructuredstudies,weimaginetheremightbeagreaterhesitancytoembracetheserendipitouslogicoftheBeyondAveragetools,especiallyinreal-worldprojectswhenproductivitydemandsarehigher.Weaimtoinvestigatethisapparentlimitationofthetools’effectivenessbyexploringhowtheresponsesprovidedcanbetherightbalanceofdisruptiverandomnessandefficientrelevance.Throughunderstandinghowtobetterframethebenefitsthesetoolscanprovidewithindifferentdesignactivities,weaimtostimulatepurposefulmomentsofunexpectedcreativereinterpretationfordesigners,aswellasslowlybroadentheirattitudesaboutthedifferentwayscomputationaltoolscanguideustobe‘productive’inthecreativeprocess.

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8.ConclusionIntheearlyphasesofthedesignprocess,embracingchanceintrusions,seemingirrelevanceandambiguitycanleadtoconsideringconceptsindifferentwaysandprovokenewideas.However,thecomputationaltoolsweareincreasinglyusinginthesephasesvalueefficiencyoverserendipity;technologieswhosefoundationsareanaverage.Thispaperexploredhowdevelopingcomputationaldesigntoolsthatembraceseemingirrelevanceandambiguitycouldinfluencethecreativeprocessintheearlyphases.

The‘BeyondAverage’approachdefinedtwodesignspacedimensions:contextuality—how‘smart’responsesfromthetoolwere—andinterpretability—howambiguoustheresponseswere.Situatedatdifferentpositionsalongthesedimensionsaretwotoolsdevelopedbytheauthors:thedesign(human)designcreativeprompttoolandtheLookingSidewaysexplorationtool.Resultsfromstudiesusingthesetoolstoprovideinspirationtoparticipantsastheyattemptedtogeneratenewideasaroundatheme(withGoogleasacontrol)showedthatcomputationaltoolswithamediumlevelofcontextualityandahigherlevelofinterpretabilitycanpositivelyinfluencethecreationofnewideas.

Imaginingthesetoolsusedasasuiteintheirdesignprocess,participantssuggestedjumpingbetweenthetoolswhentheyneededdifferentlevelsofcontextualityandinterpretability;usingtheveryambiguousdesign(human)designtooltoprovokenewseedsofideasthattheycandeeplyexploreinthemoresituatedGooglesearchengineandLookingSidewaystool.Extendingthisdiscussiontoconsidertheroleofcomputationintheearlyphasesofthedesignprocess,wesuggestthattoolswithhigherlevelsofcreativeagency—thosewithhighlevelsofbothcontextualityandinterpretability—cancontributetothedesigner’screativeagencyandbecomeamorenaturalpartnerintheseactivities.

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Beyond Average Tools

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AbouttheAuthors:

PhilippaMothersillisaPhDstudentintheMITMediaLabObject-BasedMediagroup,whereshedrawsfromdesigntheory,cognitivepsychologyandcomputersciencetodevelopnewcomputationaldesigntoolsforearlystagesinthecreativeprocess.

V.MichaelBoveistheheadoftheObject-BasedMediagroup.Heistheauthororco-authorofover100journalorconferencepapersondigitaltelevisionsystems,videoprocessinghardware/softwaredesign,multimedia,scenemodeling,userinterfaces,visualdisplaytechnologies,andoptics.

Acknowledgements:wewouldliketothankMaxLeverandJosieKufortheirtechnicaldevelopmentassistanceandtheKennedyMemorialTrustforprovidingfinancialsupportforaportionofthework.