big data and technology assessment: research topic or ... fileshifted the focus from data properties...

Preprint:Rieder,G.;Simon,J.(2017)BigDataandTechnologyAssessment:ResearchTopicorCompetitor?In:JournalofResponsibleInnovation4(2).doi:10.1080/23299460.2017.1360718.

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BigDataandTechnologyAssessment:ResearchTopicorCompetitor?

GernotRiederandJudithSimon

AbstractWithitspromisetotransformhowwelive,work,andthink,BigDatahascapturedtheimaginationsofgovernments,businesses,andacademia.However,thegrandclaimsofBigDataadvocateshavebeenaccompaniedwithconcernsaboutpotentialdetrimentalimplicationsforcivilrightsandliberties,leading to a climate of clash andmutual distrust betweendifferent stakeholders. Throughout theyears,theinterdisciplinaryfieldoftechnologyassessment(TA)hasgainedconsiderableexperienceinstudying socio-technical controversies and as such is exceptionally well equipped to assess thepremises and implicationsofBigDatapractices.However, the relationshipbetweenBigDataas asocio-technicalphenomenonandTAasadisciplineassessingsuchphenomenaisapeculiarone:BigDatamay be the first topic TA deals with that is not only an object of inquiry, but also amajorcompetitor,rivalingTAinseveralofitscorefunctions,includingtheassessmentofpublicviewsandvisions,meansandmethodsforexploringthefuture,andtheprovisionofactionableknowledgeandadviceforpoliticaldecisionmaking.OurpaperexploresthisdualrelationshipbetweenBigDataandTAbeforeconcludingwithsomeconsiderationsonhowTAmightcontributetomoreresponsibledata-basedresearchandinnovation.Keywords:BigData,TechnologyAssessment,ResponsibleResearchandInnovation,Interdisciplinarity1.IntroductionAroundtheglobe,thenotionofBigData1hascapturedtheimaginationsofgovernments,businesses,andacademics.Heraldedasakeyenablerofpublicsectorinnovation(seeEuropeanCommission(EC)2015b),acatalystforeconomicgrowthandwell-being(seeOrganisationforEconomicCo-operationandDevelopment2015),andtheemblemofanew"data-intensive"scientificparadigm(seeHeyetal.2009), there is hardly a segment of modern society that is not expected to be touched andtransformed by the ongoing "Big Data revolution" (Mayer-Schönberger and Cukier 2013). Whilewrappedinarhetoricofhypeandhope,applicationsofBigDataarenolongersciencefiction:Fromcrimeanddisasterpredictiontoonlineadvertising,fromprecisionmedicineanddiseasetrackingtoindustry 4.0, from smart cities and climate research to credit and insurance scoring, the use of

1Althoughmanydefinitionshavebeenproposed(seePress2014),there is"apronouncedlackofconsensusaboutthedefinition,scope,andcharacterofwhatfallswithinthepurviewofBigData"(Ekbiaetal.2014).OneofthemostpopularcharacterizationsisLaney's(2012,2001)notionofthe"3Vs",whichfocusesonmeasuresofmagnitudeandconceptualizesBigDataasgrowthindatavolume,velocity,andvariety.Otherapproacheshaveshiftedthefocusfromdatapropertiestonewanalyticalpossibilities,describingBigDatascienceasa"God's-eyeview"(Pentland2012)that"letsusexaminesocietyinfine-graineddetail"(Pentland2014).Incontrasttosuchtechnology-orientedperspectives,scholarsfromthesocialsciencesandhumanitieshavepointedtotheculturaldimensionofBigData,arguingthattherealnoveltyofBigDataliesinthegrowingsignificanceandauthorityofquantifiedinformationinevermoreareasofeverydaylife(seeLeonelli2014).Fromthisperspective,BigDataconstitutesacomplexsocio-technicalphenomenonthatrestsonaninterplayofscience,technology,ideology,andmythology(seeJurgenson2014;boydandCrawford2012).Itisthislatterperspectivethatwillguideouranalysis.


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computationalmeans to uncover patterns and trends in ever larger haystacks of data has foundwidespreadappeal.Significantinvestmentsarebeingmade,underpinnedbypromissorynarrativesofefficiencyandsecurity,progressandprosperity.To"unlockthevalue"and"reap[the]benefits"(EC2016a)ofBigDataappearstohaveemergedasaprimaryconcernofbothpublicandprivateentities,the incorporationof advanced analytics into virtually all areasof human life already consideredaforegoneconclusion. But the rise and spread of Big Data solutionism (see Morozov 2013b) has not remainedunchallenged.Observersinthemediaandacademia,butalsofromwatchdogorganizationsandpublicinterest groups, have called for open debate and critical reflection, pointing to unresolved issuesrelatedtoprivacyandsurveillance,biasanddiscrimination.Putpoignantly:Whatmayhavestartedasanadvertisingcampaignforthenew"testosteroneofbusinesscomputing"(Vance2010)soonturnedintoaheatedargumentaboutcivilrightsandliberties(seeUpturn2014).Thoughlarge-scaleoutcryhasbeentheexceptionratherthantherule2,publicreactionstotheSnowdenrevelations(seeLyon2014),NHSEngland'scare.datainitiative(seePresseretal.2015),orFacebook'semotionalcontagionexperiment(seeboyd2016)illustrateagrowingdiscomfortwithcurrentdatapractices.Theresultisaclimateofclash,anatmosphereofintensedistrustbetweendifferentpartiesandstakeholderswithconflictinginterests,values,anddivergingvisionsofthefuture. Thesituationisunlikelytoresolveitselfquickly:Asrapidgrowthinbothdatageneration(seeIDC2014) and the analyticsmarket (see Statista 2016) suggests, at least froma techno-economicperspective,theageofBigDatahasonlyjustbegun.Processesofdataficationandcomputerizationaresuretocontinue,anditissafetoassumethatthecombinationofdeclininghardwarecosts,risingprocessingpower,andevermoresophisticatedsoftwaresolutionswillincreasetheallureofBigData's'captureall'imperative.Withevenmoreorganizationsplanningtojumponthedatatrain(seeGartner2015),andmanypeopleexperiencingalossofcontrolovertheirpersonalinformation(EC2015c;PEW2014),furtherconflictseemsinevitable. Throughout theyears, the interdisciplinary fieldof technologyassessment (TA)hasgainedconsiderable experience in studying socio-technical controversies. Extensive research on issuesranging from nuclear power and waste management to genetically modified organisms, geoengineering, stem cell research, and nanotechnology has left the discipline with a broad set ofmethodsandtechniquestoassessandevaluatetheethical,legal,andsocialimplicationsofnewandemergingtechnologies.Thereby,theidentificationofcurrentandfuturechallenges,thefacilitationofmulti-actorinvolvement,andthesearchforbothdesirableandsustainablesolutionshasoftenbeenof central concern to the fieldand its scholars.3 Inaddition,TAasa conceptandpracticeaims tocontribute to the governanceof scienceand technologyby (a) adopting an intermediary role andfosteringdialoguebetweenpolicymakers,industry,andthepublicsphere(seeJossandBellucci2002)and(b)providingactionableknowledgeandadvicefordemocraticdecisionmakingincaseswherethestakesarehigh,factsareuncertain,andvaluesareindispute(seeKlüveretal.2015;Funtowiczand

2Citizens'passivitymayhavemultiplecauses.AsurveybyTurow,Hennessy,andDraper(2015)onconsumerdatacollection inbothdigitalandphysicalcommerce, for instance, finds thatpeople'sprovisionofpersonalinformationisnottheresultofeitherconsent,ignorance,orindifference,butratherasenseofresignationandpowerlessness,afeelingthatitisfutiletoeventrytomanageandcontrolwhatcompaniescanlearnaboutthem.3ForanoverviewoftheTAlandscapeanditsvariousstrands,seevanEstandBrom(2012)andGrunwald(2009).


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Ravetz 1993). Big Data has only recently appeared on the TA agenda4, but if the "march ofquantification"(GaryKing,quotedinLohr2012)continues,itwillprobablystaythereforawhile.

Inthisarticle,weseektoexploretherelationshipbetweenBigDataandTAfromtwodistinctperspectives:

First,wesetouttodiscusswhetherandinwhatwaysTAcancontributetothecurrentdebatearoundBigData.Inessence,itisarguedthatthefield'sexperienceinbridgingdisciplinaryboundaries,its proficiency in facilitating (upstream) public engagement, and its expertise in developingdeliberative methods for thinking about possible future scenarios may indeed prove a valuableadditiontoBigDatadiscourse,providingnotonly insight intobutpotentiallyalsoawayoutofthecurrentclimateofclash.

Second,insteadofmerelyconceivingBigDataasanewresearchtopicforTA,weshallconsidertheir relationship as one marked by rivalry and competition. Despite significant epistemic andmethodologicaldifferences,BigData'skeypromisebearsstrikingsimilaritiestothatofTA,namelytheprovision of actionable, future-oriented knowledge. Consequently, the nascent field of Big Dataanalytics–hometoagrowingnumberofsoftwaresolutionsmarketedbymajorITcompanies–maysoonchallengeTAinoneofthediscipline'scorerolesandfunctions:asascientificadvisortopoliticaland bureaucratic decision making. Ultimately, this rivalry could lead to gradual displacement,especiallyifthecomputationalapproachappearstooutperformitscompetitorbothpractically(e.g.,cheaper,faster)andepistemologically(i.e.,recommendationsbelievedtobeobjectiveandbasedonnumericalfacts).Thepotentialconsequencesofsuchashiftwillbediscussedatlength.

Inaconcludingsection,wewishtogobeyondthisscenarioofcompetitionandreplacementand instead envision possibilities for cooperation and mutual learning between TA and Big Dataanalytics.EmbeddingourconsiderationswithinthecontextofRRI,wewillinquirehowresponsibilityindata-basedresearchandinnovationmaybeachievedandponderhowmorereflexive, inclusive,andparticipatorymodesofcomputationalknowledgegenerationcouldactuallybeputintopractice.Inparticular,wewillstresstheneedforamultidisciplinaryresearchapproach,callattentiontothepoliticsofparticipationandtheperformativityof temporalities,andcommentonthechancesandpitfallsofcollaborativeknowledgeproduction.2.BigDataasaResearchTopicforTechnologyAssessmentThoughstillrelativelynewasatopicofinvestigation,withsignificantgrowthinscholarlypublicationsfrom2012onwards(seeSinghetal.2015;Youtieetal.2017),severalTA-relatedresearchinitiativesexaminingtheriseandimpactofBigDataanalyticshavealreadybeenlaunched.EuropeanexamplesincludetheGermany-basedABIDA(AssessingBigData)project5,theNorwegianBoardofTechnology'sstudyondata-driven analysis andpredictivepolicing (Teknologirådet 2015), theUKParliamentaryOffice of Science and Technology's exploration of Big Data uses across various policy areas6, orbackground documents by the European Commission's Unit for eHealth and Health Technology

4WhileexplicitreferencestoBigDatawererareintheprogramofthe1stEuropeanTAConferenceinPraguein2013(seePACITA2013;Michaleketal.2014),twoyearslater,atthe2ndEuropeanTAConferenceinBerlin,thetermhadbecomemorecommonandadedicatedsessionsoughttoinvestigatethe"GovernanceofBigDataandtheRoleofTA"(PACITA2015).5SeetheABIDAwebsite:http://www.abida.de/en(accessed12Apr.2017).6SeePOST'sBigDataprogramwebsite:http://www.parliament.uk/mps-lords-and-offices/offices/bicameral/post/work-programme/big-data/(accessed12Apr.2017).


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AssessmentonBigDatainthemedicalsector(seeEC2014).IntheUnitedStates,theWhiteHousereportBigData:SeizingOpportunities,PreservingValues(ExecutiveOfficeofthePresident2014)andthecomplementaryreportBigDataandPrivacy:ATechnologicalPerspective(President'sCouncilofAdvisors on Science and Technology 2014) are examples of high-profile technology assessmentsmeanttoinformandsteerfederalS&Tpolicy.7

Whilethe initiatives listedabovediffer inscaleandscope,theysharethecommongoalofexamining the potential impacts of Big Data from a decidedlymultidisciplinary perspective. TheGermanABIDAproject, for instance, includes five specializedworkinggroupswhoare taskedwithassessing the opportunities and challenges of Big Data from either an ethical, legal, sociological,economical,orpoliticalsciencepointofview.8Suchmultidisciplinary,whichhasbeenanintegralpartofTAprogramsfordecades9,cancontributetoBigDatadiscourseintwoimportantways:Ontheonehand,bybringing togetherexpertiseand insights fromdifferent fields, TAmayprovidea synopticoverviewofwhat is often scatteredacross variousdisciplinaryboundaries. Presented ina concisemanner,thiscollectedinformationmaythenallowTAscholarstoactas"knowledgebrokers"(Meyer2010) between the scientific and the political realm, thus strengthening what to date remains anotoriouslydifficultrelationship(seeWilsdonetal.2015).Ontheotherhand,theinfluxofknowledgeandknow-howfromdifferentdisciplinesmayprovideabetterunderstandingoftheactualsignificanceofBigDataasacomplexsocio-technicalphenomenon.Infact,asreviewarticlessuchasEkbiaetal.(2015) demonstrate, ethical and legal reflections alone are insufficient to cover the range ofconceptualandpracticaldilemmassurroundingBigData.Andifitistruethatmoderndataanalyticswillnotonly"transformhowwelive,work,andthink"(Mayer-SchönbergerandCukier's2012),butalsohowweknow(Kitchin2014),judge(Christinetal.2015),andgovern(RiederandSimon2016),aconcertedscholarlyeffortseems,indeed,indispensable. Multidisciplinarity, however, is usually only the first step; the establishment ofinterdisciplinary dialogue and collaboration being the next. Perceived as a chance to transcendresearchsilosandfacilitate"moreradicalinteractionsbetweendifferentstylesofknowledge"(Stirling2014), interdisciplinary assessments are expected to provide responses to problems that "are notsolvablebyanindividualscientificdisciplinealone"(Decker2001).Such'"wicked"(RittelandWeber1973)or"post-normal"(FuntowiczandRavetz1993)problemsituationshavebecomemorefrequentinthecurrent"ageofuncertainty" (Nowotnyetal.2001),andBigData isnoexception:Tamedbyneitherexisting law (seeBarocasandSelbst2016)nornewregulatoryapproaches (seeRubinstein2013),thesearchforhiddenpatternsandtrendsineverlarger–andincreasinglydiverse–datasetsposesahostofintricateethicalandepistemic,socialandpolitical, legal,technical,andcommercialchallenges that evade traditional problem-solving strategies. Thoughnot apanacea, issue-focusedinterdisciplinaryresearch,asincludedinmanyTAprograms(e.g.,seeDeckerandGrunwald2001),canhelpinfindingoptionsforpoliticalaction,providingpracticalguidanceforproblemsthatdonotfitinto the functional differentiation of academic disciplines.10 Yet successful collaboration can be

7AnotherexampleistherecentreportBigData:AReportonAlgorithmicSystems,Opportunity,andCivilRights(ExecutiveOfficeofthePresident2016).8ForaconciseoverviewoftheABIDAproject,see:http://www.abida.de/en/content/abida-das-projekt(accessed12Apr.2017). 9Togivebutoneexample,whenreportingonthestatusofthenowabolishedUSOfficeofTechnologyAssessment(OTA)backintheearly1980s,ProjectDirectorandSeniorAnalystFredB.Woodwritesthat"OTA'smultidisciplinarystaff[...]of80-90professionalsspansthespectrumofphysical,life,andsocialsciences,engineering,law,andmedicine."(Wood1982)10FormoreonTA's"problem-oriented"versionofinterdisciplinarity,seeSchmidt(2008).


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difficulttoachieve,requiringsignificanteffort,competence,acertainopenness,andtime.Especiallyin Europe, where higher education and research continue to be dominated by scholarlycompartmentalization11, TAmight play a crucial role in facilitating such interactions, nourishing aculture of interdisciplinarity ready to support the governance of new and emerging technologies,includingBigData. Whilemulti-andinterdisciplinarityarekeyconstituents,TAprojects,includingseveraloftheBig Data-oriented initiatives mentioned above, often seek to take an additional step: theimplementation of transdisciplinary engagement, meaning the active involvement of actors (e.g.,laypeople,specialists,interestgroups)fromdiversesocialandprofessionalbackgrounds,inanefforttobroadenthescope,gainnewperspectives,andmakethebordersbetweenscience,technology,and societymore permeable.12 Depending on the stage of a techno-scientific development, suchparticipatoryapproachesmayserve twomainpurposes13.On theonehand, in theearly stagesofdevelopment,whena technology isnewandsocietal consequencesaredifficult to foresee,publicengagement–e.g.,throughscenarioexercises(seeSelin2011),groupdiscussions(seeFeltetal.2014),or online deliberation tools (see Rommetveit et al. 2013) – can assist in generating anticipatoryknowledgeaboutpossiblefuturetrajectoriesandtheirimplications,determiningboththeplausibilityanddesirabilityofanemergingsocio-technicalarrangement.Thefocushereisonpreparationratherthanprediction,oncultivatingacapacitytoidentifyviableoptionsandalternativesintimesofgrowingcomplexity and uncertainty (see Barben et al. 2008). On the other hand, however, when acontroversial technology has already become entrenched and ex ante preparation is no longerpossible,theinvolvementofheterogeneousgroupsofactorscanprovideabetterunderstandingofthenatureof theconflict, that is, themainconcerns, thevaluesat stake, thepositions taken, theinterestsinvolved.Moreover,theinclusionofaffectedpublicscanrevealinformationaboutproblemsandissuesthatmayotherwisebeoverlooked(seeCotton2014),offeringaclearerpicturenotonlyofpossible,butofactualharmsandrisks.Insum,participatoryengagementcanbeconsideredavitalelementforamore"anticipatory"(Guston2014)and"reflexive"(Braunetal.2010)governanceofscienceandtechnology,openingbothexistingconditionsandfutureprospectstobroaderscrutinyandcriticaldebate.InthecaseofBigData,whereimpactsarealreadytangible(seeO'Neil2016)buteven bigger changes are on theway (seeDavenport 2015), such expandedmodes of inquiry andreflectioncouldproveessentialformoresustainable,sociallyrobustdevelopment.

Allinall,webelievethatTA'sexperienceinmulti-,inter-,andtransdisciplinaryresearch,itspracticalexpertiseinconsultation,deliberation,andadviceatthescience-society-policyinterface,canhelp toaddressandsuccessfullydealwith themanifoldchallengesposedbyBigData. In turn, theongoingcontroversyaboutBigDataprovidesanopportunityforTAtoprove itselfasatheoryand

11Regardingsuchcompartmentalizationinhighereducation,seeNewell(2010);regardingresearch,seePan,Boucherie,andHanafi(2015). 12Foradeeper,historicallygroundeddiscussionofsuchparticipatorytechnologyassessment(pTA),seeJossandBellucci(2002).13Infact,reasonsforpublicparticipationaremanifold(seeWesselinketal.2011).TheargumentproposedinthisparagraphreferstoDavidCollingridge'swell-known"dilemmaofcontrol".Collingridge(1980)states:"Thesocial consequences of a technology cannot be predicted early in the life of the technology. By the timeundesirable consequences are discovered, however, the technology is often so much part of the wholeeconomicsandsocialfabricthatitscontrolisextremelydifficult."LikeCollingridge,TAsearchesforwaysandmeans to deal with and, both in theory and practice, overcome this quandary. For an insightful analyticaldiscussionofthedilemma,itsassumptionsandrelationshiptoTA,seeLiebertandSchmidt(2010).


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practice,demonstrating itsvalueandrelevanceforthedemocraticgovernanceoftechno-scientificinnovationandchange.

Moderndataanalytics,however,arenotonlydestinedtobecomeanimportantresearchtopicforTA,theymayalsoemergeasaseriouscompetitortoTA,rivalingthefield inseveralof itscorecompetencies, including the assessment of public views and visions (i), means and methods forexploringthefuture(ii),andtheprovisionofactionableknowledgeandadviceforpoliticaldecisionmaking(iii).Following,weshallelaborateonboththeprogrammaticandepistemologicalsimilaritiesanddifferencesbetweenBigDataandTA,highlightingconceptualincommensurabilitiesaswellasthepotentialformethodologicalcomplementarityandresearchsynergy.

3.BigDataasaCompetitionforTechnologyAssessment3.1.TheassessmentofpublicviewsandvisionsWhile TA was initially conceived as a "rational-scientific tool" (Thompson Klein 2001) that wouldprovide policy makers with "competent, unbiased information" concerning "probable impacts oftechnology"14, the field's focus has since shifted from mere risk-based assessments to greaterconsiderationofpublicacceptance(seeAssefaandFrostell2007)andsocialdesirability(seeBennettandSarewitz2006).Supportedbyabroadvarietyofsurveyandengagementmethods15,numerousresearchprojectshave sought to investigatepeople's valuesandbeliefs, but also theirhopesandconcerns regarding specific techno-scientific developments. However, there are certain problems:Quantitative survey research, for instance, has been criticized for relying on narrow 'tick-box'questionnaires that fail to account for the plurality and complexity of laypeople's thinking (seeMacnaghtenetal.2010)andfortheparticular'versionsofreality'suchsurveysenact(seeLaw2009).Qualitative engagement exercises, in comparison, have come under fire for being slow and time-consuming16, forgrantingtoomuchauthoritytothenew"expertsofcommunity"(Rose1999)andtheir "technologies of participation" (Chilvers andKearnes 2016), and for issues of legitimacy andrepresentativeness(seeLafont2015).

Inviewof suchcriticism, thegrowing interest indigitalmethods forcontroversymapping,opinionmining,andsentimentanalysis shouldnotcomeasasurprise.17PairedwiththeepistemicpromisesofBigData(seeRiederandSimon2017;Kitchin2014)suchcomputationaltechniquesmayindeedseemlikeanoffertoogoodtorefuse:Advertisedasfastandcheap,BigDatatoolspromisereal-timeanalysis,claimingtoprovideguidanceandorientationatabargainprice.Furthermore,bygleaningdatafromonlinesources–e.g.,fromsocialnetworkingsitessuchasFacebookorTwitter–

14QuotedfromtheU.S.CongressTechnologyAssessmentActof1972,PublicLaw92-484,§2(d)and§3(c),which created the now defunct Office of Technology Assessment (OTA), see:https://www.gpo.gov/fdsys/pkg/STATUTE-86/pdf/STATUTE-86-Pg797.pdf(accessed12Apr.2017).15Foraselectiveoverviewofpublicengagementmethods,seeParliamentaryOfficeofScienceandTechnology(2001);forareviewandcriticaldiscussionoflarge-scalesurveyresearch–anditsparadigms–seeBauer(2008).16Foralistofparticipatorymethods,includingtimeandcostestimates,seeInvolve(2005)andtheParticipationCompass:http://participationcompass.org/article/index/method(accessed12Apr.2017).17AsanindicationofthisinterestinaEuropeanpolicycontext,considerstudiessuchasthecommissionedreportBigDataAnalyticsforPolicyMaking(EC2016b),eventssuchastheEurActivstakeholderworkshopBigData&Policy Making, see http://www.euractiv.com/section/digital/video/big-data-and-policy-making/, or researchinitiativessuchastheFrameworkProgramme7projectsSENSEI,seehttp://www.sensei-conversation.eu/,andEuroSentiment,seehttp://eurosentiment.eu/(allaccessed12Apr.2017).


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computationalmethods are said to bridge the qualitative-quantitative divide, capturing an entireconflictordebateinfulldetail.Finally,presentedasadisinterestedreadingofreality,theBigDatamarketingnarrativefeedsintotheidealof"mechanicalobjectivity"(DastonandGalison2010),thusseeminglysolvingtheproblemofindividualorinstitutionalresearchbiasbypurelytechnicalmeans.Whilescholarsfromdifferentdisciplinarybackgroundshavepointedoutthepracticallimitationsandconceptualflawsofthisheraldedmethodologicalrevolution(e.g.,seeCrawford2013;Mustafarajetal. 2011), theability tomeasurepublic attitudesdirectlyandwithoutdelaypresentsa compellingprospect forapoliticalsystemfacing issuesof trustanduncertainty.AndeventhoughTA'srole infacilitatingpublicparticipationinscienceandtechnologygovernancegoeswellbeyondthecollectionofviewsandopinions,thesystematicmonitoringofuser-generatedonlinedataforfeedbackgatheringandtrendanalysismaysoongivetraditionalengagementmethodsarunfortheirmoney.Inasocietywheremoreandmoreofpeople'sinteractionshavemigratedtotheWeb,theopportunitiesforsuchresearchproliferate–andpolicymakersaretakingnote(seeGrubmülleretal.2013).ThequestionofhowTAshouldrespondtothischallengewillthusbecrucialtothefield'sfuturedevelopment.3.2.MeansandmethodsforexploringthefutureTechnology assessment's relationship to the future is the next potential site of competition. Asmentionedabove,theorientationtowardsthefutureisacentralelementofTAandhasbeenaguidingissuefromitsverybeginning.Yet,followingsomedisillusionmentwithpositivisticanddeterministic"prognosticism"(Grunwald2009),therehasbeenashiftfromnotionsofearlywarningandcontroltothoseofshapinganddesigning(seeGrunwald2014).ThefutureorientationofTAbecomesobviousbothinmethodologyandconceptualwork:Ontheonehand,empiricalstudiesonthesocietalimpactsoftechnologymakeuseofanabundanceofforesightmethodssuchasDelphisurveys,roadmappingexercises, or scenario development (see Porter 2010). On the other hand, conceptual work ontechnologyfutureshasblossomedinrecentyears,andtherehavebeennumerousattempts–bothfrom within and beyond TA – to conceptually grasp the dynamic and performative relationshipsbetweenpast,present,andfuture(seeEsposito2007;BrownandMichael2003).Twoprominentnon-deterministic approaches at the core of the TA movement are the concept of "anticipatorygovernance"(Guston2014)andthenotionof"technologyfutures"(Grunwald2012).Whiletheformeradvocates broad-based capacity building to manage emerging technologies as long as suchmanagementisstillpossible(seeGuston2008),thelatterstressesthevalueoftechnologyfuturesasa common point of reference between developers, political actors, and the wider public, thusemphasizingtheircontributiontoasustainableco-evolutionoftechnologyandsocietybystimulatingcriticalreflectionanddebate(seeGrunwald2012).Whatunitesthetwoapproaches–andTAforesightpracticesingeneral–isanunderstandingofthefutureasopenandmalleable,assomethingthatcanbe steeredand shaped,not "determinedbynatural necessities, but contingent and influencedbyhumanaction"(Voßetal.2006).Avoidingany'crystalballambitions',contemporaryTAconceptualizesforesight and anticipation as a fundamentally democratic practice, an inclusive societal learningprocessthatismeanttoreducethecostsoflearningbytrialanderror.

Thereductionofcoststhroughfuture-orientedanalysisisalsooneofthemainsellingpointsofBigData.Themethodsemployed,however,differconsiderably:Insteadofdeliberativeforesight,BigData specializes inpredictive forecasting; insteadofnegotiatingplausible future scenarios,BigDatatechnologiesestimateprobablefuturetrajectories.Inessence,historicalandnear-timedataareusedtoidentifypatternsandtrends,markinganepistemologicalshiftfromfuturesassociallycreated


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(seeAdam2005)tothefutureasanobjectofmachinecalculation.Thespiritofpositivismthusreturns(seeJurgenson2014),anditappearsmorepowerfulthanever,fueledbyamassiveincreaseindataavailabilityandadvancedtoolsandtechniquestoprocessandleveragethem.TherealchallengeforTA,however,isarguablynotsomuchtheupcomingfieldofdatascience18,but,onceagain,theBigData imaginary, which promises almost universal applicability (see Anderson 2008) and therestorationofcertaintyinuncertaintimes(seeHardy2013).Byrenderingthefutureknowableanditsoutcomeoptimizable19,BigData"revitalize[s]thepromiseofpredictionacrosssocial,political,andeconomicworlds"(AradauandBlanke2016),becomingbothproductandenablerofanew"regimeof futurity" (Ekbia et al. 2014), in which slower and less accurate methodologies are consideredobsolete.Inasocietythatincreasinglythinksandlivestowardsthefuture,thatismarkedbyaconstantneed to conquer and colonize the 'not yet' (see Adams et al. 2009), technology assessment'sdeliberativemodesoffutureengagementmaysoonfindthemselvesoutgunnedandoutpacedbythegrandclaimsofthealgorithmicforecastingindustry.Onceagain,anopendiscussionofhowTAasafuture-orienteddisciplineshouldandcouldrespondtothischallengeseemsparamount.

3.3.TheprovisionofactionableknowledgeandadviceWhile"anticipating futuredevelopmentsand their impacts" isakeyobjectiveofTA, the fieldalsoseeksto"accommodatesuchinsightsindecisionmakinganditsimplementation"(Rip2012).ThefocusonprovidingactionableknowledgeforpoliticaldecisionmakinghasbeenamajorconcernofTAsinceitsformalinceptioninthe1970s.Backthen,thenowdefunctOfficeofTechnologyAssessment(OTA)was commissioned to advise the US Congress in matters of science and technology. While theexecutivebranchof theUSgovernmentcould relyonanextensiveapparatusofdepartmentsandagencies,Congressasthelegislativebranchwaslackingsuchresources.Thus,acrucialfunctionofOTAwas to re-establish the knowledge/power balance between the government's legislative andexecutivebranches(seeSadowski2015;Bimber1996).Thefocusonactionableknowledge,however,becomes apparent not only in the specific case of parliamentary TA, which aims to "strengthenrepresentativedemocracybytimelyinformingMPsaboutthepotentialsocialimpactsoftechnologicalchange" (van Est andBrom2012), but alsowhenparticipatory TA is used as ameans tomediatebetween the interests of different stakeholders, for instance in the context of selecting sites fornuclearwastedisposal(seeHockeandRenn2009).Thedistinctionbetweenconsultation,ontheonehand,anddecisionmaking,ontheother, iscrucial for thedisciplinaryself-understandingofTA. Inordertoremaintrustworthyinitsadvisoryfunction,TAaimsatprovidingindependent,high-qualityknowledge about techno-scientific developments and their potential social, ethical, and legalimplications.Itdoesnot,however,activelyparticipateinthedecisionmakingprocess.20Moreover,TAasadisciplineiswellawarethattheimpactofitsadvicevariesgreatlyandishardtopredict.Whilesomereportsmaydirectly influenceparliamentarydecisions,othersmaygettuckedaway infillingcabinets,nevertobereadagain.Thisrelativeopennessanduncertaintyshouldnotbeseenasafailure

18Datascientistsareusuallywellawareofthevariouslimitationsoftheircraft.ForabalancedaccountofpredictionintheeraofBigData,seeSilver(2012).19Consider,forinstance,IBM'sadvertisingsloganfortheirpredictiveanalyticsproducts,whichpromptscustomersto"optimizethefuturewithbetterdecisionstoday".See:http://www.ibm.com/analytics/us/en/technology/predictive-analytics/(accessed12Apr.2017).20ForanoverviewofthedifferentpracticesandinstitutionsofparliamentaryTAinEurope,seeNentwich(2016).


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of thediscipline,butasa testimony to its facilitativeand supportive rather thandeciding societalfunction. InthecaseofBigData,thedistinctionbetweenconsultationanddecisionmakingisfarlessobvious:WhileBigData technologiesare said toprovide insightandguidance forhumandecisionmaking,theyareincreasinglyusedtogeneratedecisionrecommendationsoreventakeactionontheirown(seeCitronandPasquale2014).Whatcanthusbeobservedisagradualshiftfromdescription(i.e., data reporting) and prediction (i.e., identifying trends) to prescription and automation (seeDavenport2015).Whereasprescriptiveanalyticsaremeanttosuggestactionsand"tellyouwhattodo" (Davenport2013), themovetowardsautomationshifts thepower–andburden–ofdecisionmakingfromthehumanactortoeversmarterprogramsandmachines.Inthelattercase,algorithmicsystems do not merely participate in decision processes, but perform certain actions with no orminimalhumanintervention.Andeventhoughafullyautomatedstatemaystillbeadistantutopianordystopianvision(seeForster1909),thereisclearindicationthatthedemandforsuchsolutionsinthepublicsectorisgrowing(seeHartzogetal.2015).Insuchacontext,amajortaskforTAwillbetocritically assess and question the "prominence and status acquired by data as a commodity andrecognizedoutput"(Leonelli2014)aswellastochallengethe"widespreadbeliefthatlargedatasetsofferahigher formof intelligence […],withanauraof truth,objectivity,andaccuracy" (boydandCrawford2012). Inaddition,however, the fieldwillalsohavetodevelopstrategies tomaintain itsrelevance in a crisis-ridden political environment that longs for seemingly clean, unambiguousknowledgeandadvice.Tobeblunt,therecentpushfor"dataforpolicy"(EC2016c)and"evidence-informed decision making" (EC 2015a) does not aim to raise the budget for traditional publicengagementexercises,butencouragesthedevelopmentofcomputationalsolutionsthatmaymakesuchmethodologies appear increasingly redundant.Going forward, fast-paced innovation and theongoingdataficationofsocietywillmakethisanevenmorepressingmatterofconcern.4.Discussion:TowardsResponsibleData-BasedResearchandInnovation TherelationshipbetweenBigDataasacomplexsocio-technicalphenomenonandTAasadisciplineassessingsuchphenomenaisapeculiarone:BigDatamaybethefirsttopicTAdealswiththatisnotonly anobjectof inquiry, but also amajor competitor, rivalingTA in several of its core functions.Having outlined a narrative of competition in the previous section,we nowwant to conclude bysketchinganalternativewayforward,onethatconsiderstherelationshipbetweenBigDataandTAwith respect to the concept of RRI. In essence, we believe that TA's focus onmulti-, inter-, andtransdisciplinaryresearch, itsreflexiveorientationtowardsthefuture,anditspracticalexpertiseinprovidingpolicyadvice,canhelptoaddressandsuccessfullydealwiththemanifoldchallengesposedbyBigData.Indoingso,TA-basedanalysismayprovidevaluableinsightandsupportforthealignmentofBigDatagovernancewith theaimsandgoalsofRRI, awidespreadpolicyagenda theEuropeanCommissionbroadlydefinesas"anapproachthatanticipatesandassessespotentialimplicationsandsocietalexpectations[…],withtheaimtofosterthedesignofinclusiveandsustainableresearchandinnovation"(ECn.d.b).WhilewedonotwishtoengageinadetaileddiscussionofRRIasaconceptandfundingstrategy(see,however,Simon2017;2015),wedoseektohighlightanumberofcentralissuesandconcernsthatmayrequirespecialattentionwhenmovingBigDataundertheRRIumbrella.4.1.MultidisciplinaritybeyondELSIfication


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AstherangeoftopicsandissuescoveredinjournalssuchasBigData&Societyindicates,thescopeandcomplexityoftheBigDataphenomenonextendswellbeyondthepurviewofanysingleacademicdiscipline. Meaningful assessments of societal impacts will thus require the collaboration ofresearchers from different fields, not only contributing their domain-specific knowledge, but alsoengagingincross-disciplinaryinvestigations,consideringcomplexsocio-technicalentanglementsfromvariousanglesandperspectives.Asargued,TAasananalyticpracticeiswellequippedforsuchatask,buttheselectionoftherelevantscientificdisciplinesisbothcrucialandtrickyandshouldbemadewithcare(seeDecker2004).Whilethechoiceultimatelydependsonthequestiontobeansweredandtheproblemtobesolved,inthecaseofBigData,thescopeoftraditionalELSIresearchoftenwillnotsuffice.Inparticularpolitical(Morozov2013a),economic(Newman2015),andepistemic(RiederandSimon2017)premisesandimplicationsshouldbetakenintoaccount,andsometechnicalexpertisemayprovenecessarywhendealingwithmattersrelatedtoadvancedcomputationalmethodssuchasdataminingormachinelearning(e.g.,seeBarocasandSelbst2016;Burrell2016).Thus,inordertotrulygrasptheimpactsandconsequencesofBigDataandpaththewayformoreresponsibledata-basedresearchand innovation, findingthe 'right' researchpartnersandestablishingahigh-qualityexchangerelationshipwillbekey.

4.2.PublicEngagementandthePoliticsofParticipationTheidea(l)ofRRIemphasizestheneedfor"deepeningtherelationshipbetweenscienceandsociety"(European Parliament and Council 2013) by "includ[ing] multi-actor and public engagement inresearch and innovation" (EC n.d. b), fostering "dialogues between researchers, policy makers,industry and civil society organizations, NGOs, and citizens" (EC n.d. a).While the general aim of"bringingonboardthewidestpossiblediversityofactors"(ECn.d.a)maybedemocraticallylaudable,andTAcertainlyhasalottoofferinthisregard(seeSection2),thespecificmodesandmodalitiesofengagement remain a major issue of concern. Despite a rhetoric of openness and inclusion,consultationexercisesarefrequentlydesignedasone-way, top-downpubliceducationapproacheswhere participants are taught about scientific facts, expert knowledge is given primacy over layexpertise,andastrongcommitmenttoconsensusstiflesdeliberativedisagreement.GivenBigData'snowwell-documentedpotentialforcausingharm(seeO'Neil2016)andpeople'sgrowingdiscomfortwiththewidespreadapplicationandimpactofdataanalyticsinnumerousareasoflife(seePew2015),suchstrategiesofappeasementseembothfutileandutterlymisplaced.Instead,controversiesshouldbeembracedassitesofsocial learningwherecitizenscanshareanddiscusstheirexperienceswithspecific services and applications (see Rip 1986). Moreover, measures should be taken that theoutcomes and findings of deliberative engagements can actually affect the regulationof newandemerging technologies, which means that even stopping certain developments – e.g., throughtemporaryorpermanentmoratoria–mustbeconsideredarealoption.Otherwise,publicconsultationandstakeholderinvolvementrisksbecomingafarceandmayrightlybeaccusedasameanstosilencecriticalvoicesandfabricateconsent.4.3.PerformativeTemporalitiesandContestableFuturesAsoutlinedinSection3,TAandBigDatapracticesshareacommoninterestinthefuture.WhileBigDataismainlydiscussedasaforecastingtechnologythatmaysoon"predictoureverymove"(HassaniandSilva2015),TAisrootedinthebroaderandlessdeterminantbutequallyforwardlookingtradition


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of foresight (seeHarper2013), focusingondifferent stakeholder's visions,expectations,and fearsratherthanstatisticalpatterns.FromaTAperspective,thefuturecannotbediscovered,buthastobecreatedandconstructed–forinstancethroughscenariobuilding–resultinginapluralityofpossiblefuturesandpathsthatareopentopublicscrutinyanddeliberation.Inaddition,TAemphasizestheinterconnectednessbetweenpast,present,andfuture,acknowledgingthatwecanonlythinkaboutfuturesaccordingtoourpresent-day'sknowledgeandthatthewayshowfuturesareconstructedaredecisive for their content (seeGrunwald 2010). Such a reflexive stancemayprove valuablewhenassessing the performativity and politics of Big Data forecasts, which aremarked by a shift frompredictiontoprescription,nolongerlimitedtotheconfinesofprognosis,butactivelytellingpeople"what they should be doing next" (Eric Schmidt, quoted in Jenkins 2010). Ultimately, Big Data'spredictivepowermayenablea"newphilosophyofpreemption"(KerrandEarle2013),whichforestalls(human) action based on algorithmic estimates. If we allow such systems of digital regulation toproliferate,weneedtobeverysureabouttheirepistemologicalpremisesaswellastheirpotentialethical,social,andpoliticalimplications.4.4.Actionable,Situated,andInclusiveKnowledgeGiventheprevalent"trustinnumbers"(Porter1995)withinpoliticalandadministrativecircles,ontheone hand, and BigData proponents' claims of predictive superiority and analytical neutrality (seeAnderson2008),ontheother,TAmaysoonfaceanewcompetitorinprovidingguidanceandsupportforpublicpolicy.Butpolicymakersshouldbeawarethattheverykindofknowledgetheyreceivemaydiffer considerably between the two approaches:While TA focuses on collective problem solving,discussions and critical deliberation (see Abelson et al. 2003), Big Data methods tend to look atnetworksandopinionsfromadistance(seeMoretti2013).Two-way interactions,mutual learning,andexternalsubject-matterexpertisearekeyelementsofTA,butmuchlesssoindataminingandcomputationalanalytics.However,ratherthansubstitutingonefortheother,webelievethatbothfieldscanlearnandbenefitfromoneanother.Forinstance,whereasBigDatamethodscouldexpandthebreadthoftraditionalscopingexercises(seeGandomiandHaider2015),facilitatethetrackingoftrends(Nguyenetal.2016)andpublicsentiment(seeCambriaetal.2014),andhelpmapthedynamicsofcontroversiesovertime(seeLansdall-Welfare2014),TAcoulduseitsmethodologicalknow-how,itsreflexivecapacities,anditsexperienceinpolicyadvicetomakeBigDataRRI-ready.21Intheend,diligentlysupervisedmixed-methodsapproachescouldcontributeto thetheoreticalandmethodologicaldevelopmentofbothfieldsaswellastothegeneraladvancementofresponsibledata-basedresearchandinnovation.Inthisconcludingsection,wehavearguedthatBigDatapracticesmayclearlybenefitfromtheinsightsandexperienceofTAandhavepointedtowardscertainissuesandconcernsthatmayprovecrucialwhenseekingtoalignBigDataresearchwithcentralRRItenets.Whileouranalysishadacriticaledgeandsoughttodebunkcertainexaggeratedhopesandclaimsmainlyvoicedbyindustrystakeholders,wedowishtoacknowledgethegreatsocial,economic,andacademicopportunitiesthatBigDataand

21Inthisrespect,TAcouldalsolearnfromthedigitalmethodscommunity,whichhasemployedWeb-basedtoolstomap controversies around, e.g., globalwarming (Weltevrede andBorra 2016), biofuels (Eklöf andMager2013),orGMfood(MarresandRogers2000),embracingtheepistemicopportunitiesofonlinedataminingwhileremainingattentive topotential limitationsand theperilsof competitivemarketization (seeRiederandSire2014).


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relatedmethodsprovide.ThereisnodoubtthatBigDatatoolscanbeusedforthecommongood,butthere are pitfalls along theway thatmust be thoroughly understood and addressed. Thus, whileopportunitiesshouldbeexploited,thisneedstobedoneinaresponsible,sociallysustainablemanner.Throughoutthepaper,wehavealsoarguedthatBigDataposesaconsiderablechallengetoTA,rivalingthefieldinseveralcorefunctions,includingtheassessmentofpublicviewsandvisions,meansandmethods forexploringpossible future trajectories,and theprovisionofactionableknowledgeandadvice forpoliticaldecisionmaking.Webelieve that inorder to stay in thegame,TAwill have toengagewiththenewmethodsandtechniquesofferedbyBigDatatechnologies.Suchanengagementshouldinclude,butnotbelimitedto,criticalreflection.Instead,TAshouldconsiderformingcoalitionsof mutual learning, for instance by including data scientists into future project designs, therebyexpanding itsmultidisciplinaryexpertisebyyetanotherapproach.Whatweproposeisathirdwaybetween industry hype and Big Data doom and gloom, one that acknowledges the value of datascienceasapowerfulepistemicpractice,thatisopentotheopportunitiesgrantedbytheproliferationofdigital(social)data,andthattakesaproactivestanceindevelopingtoolsandmethodsthatfunctionasbestpracticeexamples. Ifdoneright,moderndataanalyticscouldbecomeanallyratherthanacompetitortothefieldoftechnologyassessment,potentiallyextendingthescope,speed,andqualityof discourse, dispute, and trend analysis. If disregarded and left to the proprietary discretion ofcommercialproducts,however,BigDatamaynotonlygrowtochallengetheTAcommunity,butcouldalsoposeaconsiderablethreattothecoreprinciplesofResponsibleResearchandInnovation.FundingThisworkwassupportedbyAustrianScienceFund(AT)[grantnumberP23770].NotesoncontributorsJudithSimonisFullProfessorforEthicsinInformationTechnologiesattheUniversityofHamburg.Sheisco-editorofthejournals"Philosophy&Technology"and"BigData&Society".ShealsoservesontheexecutiveboardsoftheInternationalSocietyforEthicsandInformationTechnology(inseit.net)andtheInternationalAssociationforComputingandPhilosophy(iacap.org).GernotRiederisaPhDfellowattheITUniversityofCopenhagen.HisdissertationinvestigatestheriseofBigData inpublicpolicyand thesocial,ethical, andepistemological implicationsofdata-drivendecisionmaking.Gernotservesasanassistanteditorforthejournal"BigData&Society".ReferencesAbelson,Julia;Forest,Pierre-Gerlier;Eyles,John;Smith,Patricia;Martin,Elisabeth;Gauvin,Francois-Pierre(2003)DeliberationsaboutDeliberativeMethods:IssuesintheDesignandEvaluationofPublicParticipationProcesses.In:SocialScience&Medicine57(2),239–251.Adam,Barbara(2005)FuturesintheMaking:ContemporaryPracticesandSociologicalChallenges.PaperpresentedattheASAconference.Availableat:http://www.cardiff.ac.uk/socsi/futures/conf_ba_asa230905.pdf(accessed12Apr.2017).Adams,Vincanne;Murphy,Michelle;Clarke,AdeleE.(2009)Anticipation:Technoscience,Life,Affect,Temporality.In:Subjectivity28(1),246-265.


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