digitaleurope's views on the artificial intelligence, machine … · 2019. 1. 10. ·...

DIGITALEUROPERuedelaScience,14-1040Brussels[Belgium]T.+32(0)26095310F.+32(0)24310489www.digitaleurope.org|[email protected]|@DIGITALEUROPETransparencyregistermemberfortheCommission:64270747023-20

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DIGITALEUROPE'sviewsontheArtificialIntelligence,MachineLearningandRobotics

Brussels,10May2017

Tableofcontents:

1. Introduction

2. Definitions

3. ExamplesofAI/MLandroboticsusesa. Agricultureandfoodb. Cybersecurityc. Energyd. Environmente. Healthcaref. Mobility,transportationandautomotiveg. Onlineservices

4. Policychallenges

a. LabourMarketsandProductivityb. Liabilityc. Responsibletechnologydevelopmentd. Transparency&trust

5. Whatcanpolicy-makersdo?


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1. Introduction

DIGITALEUROPEhaswitnessedtheincreaseinpolicymakers’interestintopicsrelatedtoartificialintelligence(AI),machinelearning(ML)androbotics.Wethinkthisinterestistimelyandjustified,asthesetechnologiesarealreadybeingappliedinmanysectorsofoureconomiesandsocieties.Healthcare,agriculture,transportation,supplychain,cybersecurity,educationandmany other sectors are embracing them,with consequential benefits to society – throughnew services andtransformationalinnovation.Clearly,thepotentialofAI/MLandroboticsishuge.Nevertheless,wealsothinkitisappropriatetoacknowledgethechallengestheseapplicationsposewithregardtocivilrights,privacy,securityandjobs.AI/MLandroboticsarenotnew.Althoughweareataveryearlystageofthisexcitingjourney,thesedata-drivensystemsandproductsarealreadyareality.Automationhasbeenafactoroftransformationthroughouthistory,regardedbymanyasadriverofsocietalandeconomicprogress.Thesameistrueforsoftware.ManyAI/MLandrobotics-basedapplicationsareusedbyasignificant proportion of society, and are accepted as part of daily life. Still,there is a trend to look at thesetechnologies with diffidence, not just in Europe but worldwide. AI/ML and robotics are discussed in a veryemotionalcontext.DIGITALEUROPE’s objective is to contribute to the discussion in Europe by raising awareness of what suchsystemscandoandwheretheir limitationsare.Weenvisageproductsandservicesthatwillhelppeople leadbetterandsmarter lives, increaseconsumerchoiceandultimatelyhelpaddress theworld’sbiggerandmorecomplexchallenges.Webelievethiscanonlybeachievedthroughacollaborativeprocessthatreliesuponopenresearch,theexchangeofbestpracticesandstakeholderdialogue.WebelievethediscussioninEuropeshouldmovetowardsamorefactual,rationalandevidence-basedapproach.Best-practicesandconcreteexamplesofhowAI/MLandroboticscanbenefitsocietyanddriveinnovationarethebesttoolstohelppolicymakersunderstandthesetoolsandidentifybetterwaysforward.DIGITALEUROPEwelcomestheopportunitytocontributetothisgoalbysharingourmembers’directexperience.Inthispaper,wewillhighlightthepotentialofAI/MLandrobotics,whilesheddinglightontheindustry’sapproachtosomeofthechallenges.


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2. Definitions

ArtificialIntelligenceArtificial Intelligence (AI) is often used as a general term to describe different technical concepts.We coulddescribeAIascomputerscienceexploringthepossibilityofemulatinghumanthinking.This includesdifferentbranches,whichareoftenusedinparalleltocreatecomplexsystems.WhenpeopletalkaboutAI,though,theyareusuallytalkingaboutMachineLearning.MachinelearningMachinelearning(ML)isabouttrainingcomputerstolearnfromexamples.MLallowscomputerstofindinsightfromdatawithoutbeingexplicitlyprogrammedwhere to look.Thismeansmachinesdonotneed tobepre-programmedtocarryoutataskinacertainway.Theymakerecommendationsastothebestcourseofactionbydrawinglessonsfromthedatatheyweretrainedon.Theiterativeaspectofmachinelearningis importantbecauseitallowsmodelstoadaptastheyareexposedtonewdata.Thisgoesbeyonde.g.dataminingwhichisabout identifyingpatterns inasetofdata,butwithout theambition tobeable topredictbehaviours innewsituations.ML is not a new science, themathematics havingbeen there for decades. It is nowgaining freshmomentum because enough computing power is available to make it work both in terms of the power ofmachinesandofnetworks.RoboticsRoboticsisthescienceofmakingmachinesthatmove.Therearemanydefinitionofwhatconstitutesa“robot”.WhileISO8373:2012definesitasan“actuatedmechanismprogrammableintwoormoreaxeswithadegreeofautonomy,movingwithinitsenvironment,toperformintendedtasks”,futurerobotswilldefinitelygobeyondthis.Roboticstechnologies,combinedwithAIandMachineLearningwillrevolutionizeautomation.Today,robotsare often classified as industrial or service robots, depending on their application. Future “smartmachines”activelydoingworkwillassumeverydifferentrolesandappearancesinmanyapplicationdomains.


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3. ExamplesofAI/MLandroboticsuses

a. AgricultureandfoodAutomaticproductsorting:ThesonoftwoJapanesefarmersusedmachinelearningtobuildasystemallowingthemtosortthedifferentkindsofcucumbersautomatically.Thisrelievedhismotherfromthetaskofsortingthemmanuallyandallowedtoallocatetimetomoreimportantactivities.Monitoringindairyindustry:DutchcompanyConnecterrausessensorsandMLtoprovideahealthmonitoringserviceforthedairyindustry.Awearabledevicemonitorstheherdandtransmitsthedatatoacloudplatformforanalysis and prediction of behavioural patterns. This allows farmers to free up labour time, improve milkproductionandsavemoney.Productsafetymontoring:JapanesefoodsafetycompanyKewpiebuiltasystemtohelpworkersidentifydefectivefoodproducts.AnAI-poweredvideofeedalertstheworkerwhenadefectisfound,soitcanberemoved.Plantandfieldreal-timemonitoring:TheIoTplatformcanintegratedroneimageryandsensordatafromsoilandenvironment sensors. The insights generated from that data can help farmers make better decisions andthereforedrivehighperformance.Precision Crop Management Testbed developed by SkataSeed and an IT company can monitor the plantenvironment24/7inreal-time,analyzingdroneaerialimageryandsensordatafromsoilandenvironment,gettinginformationsuchasplanthealth,soilmoisture,CO2,sunlight,rainfall,air,humidityandmore.Ithelpsfarmerstoimprovecropproductivityandoperationalefficiencybytakingstrategicactionsguidedbytheseinsights.

b. CybersecurityEveryyearthereareaboutthe75,000+documentedsoftwarevulnerabilities,10,000+securityresearchpaperspublishedand60,000+securityblogspublishedeachmonth.AIismakingitpossiblenowtoquicklyinterpretthisdata— createdby humans for humans—and integrate itwith structureddata from countless sources andlocations.Thismeansthatactioncanbetakenwithinminutesthatwouldpreviouslyhavetakenahumananalysthoursordays.CognitivesecurityofferedforexamplebyIBMWatsonforSecurityusesintelligenttechnologieslikemachine learning and natural language processing tomimic theway the human brain functions. It getsstrongerovertime,learningwitheachinteractionandgettingbetteratproactivelystoppingthreats.


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c. Energy

Gasturbines:Siemensisimprovingtheoperationofgasturbines.Thesystemlearnsfromoperatingconditionsandotherdataandcanthussignificantlyreducetheemissionsoftoxicnitrogenoxides-withoutsacrificingtheperformanceoftheturbineorshorteningitsservicelife.Windturbines: AI isalsoappliedtooptimizetheoperationofwindturbines,whichadjustthepositionoftherotorsautonomouslytothechangingwind,sothattheyieldofawindparkincreases.Datacenters:ApplyingDeepMind’smachinelearningtoGoogledatacentres,machinelearningsystemwasabletoconsistentlyachievea40percentreductionintheamountofenergyusedforcooling,whichequatestoa15percent reduction in overall PUE overhead after accounting for electrical losses and other non-coolinginefficiencies.OilandGas:Criticalbusinessoperationsintheoilandgasindustry,can’taffordtoshutdownwhenanetworkconnectionis lost,Theyoftencan’taffordtheextratimeittakestosenddatatothecloudandback.Remoteoperations—suchasanoilplatforminthemiddleoftheNorthSeaor intheGulfofMexicooraminedeepunderground— can now evaluate their remote sites for hazards, triggering action based on safe operatingdirectivesattheedge.Weatherconditionsmonitoring: Externalconditionssuchasweathercanbemonitored, triggeringmitigationplans.Andassetperformancecanbeevaluatedatthepointofmonitoring,drivingcorrectiveactionandreducingwastedwork.Companiesmanagingfleetsofassets,whethertrucks,tankersoroilrigs,willnowbeabletomonitorgeographicallydispersedassetsbychoosingpreciselywhatdataisbestanalyzedattheedgeandwhatshouldbetransmittedcentrally.IBMandCiscohavetakenaveryimportantfirststeponthepathtowardhelpingbusinessesacrosstheenergyindustryharnessthevalueofIoT,attheedge,inthecloudorwherevertheirneedsdemand.

d. EnvironmentAuniversityresearchteamteamedupwithcomputerscientiststotrackthepopulationofseacows.UsingtheopensourceplatformTensorFlow,theybuiltadetectorthatcouldlearntofindseacowsinphotosoftheseasurface instead of doing it manually. Tracking threatened animal populations on a large scale can helpconservationistsidentifyneedsintermsofprotectingtheirhabitats.


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e. Healthcare

Evidenced-based,patient-centrictreatmentoptions:Theamountofresearchanddataavailabletohelpinformcancertreatmentsisgrowingexponentially.Yetthetimecareteamshavetoconsumethisinformation—locatinginsightsspecifictoeachpatient’suniqueneedstopotentiallyimprovetreatmentoutcomes—ismorelimitedthanever.IBM’sWatsonforOncologyhelpsphysiciansquicklyidentifykeyinformationinapatient’smedicalrecord,surfacerelevantarticlesandexploretreatmentoptionstoreduceunwantedvariationofcareandgivetimebacktotheirpatients.Diabeticretinopathyisoneofthefastestgrowingcausesofblindnessglobally.It’spreventable,butdevelopingcountriesdon’thaveenoughophthalmologiststoprovidescreeninganddiagnostics.Machine learningcanbeusedtoscanpicturesofpatients’retinas,andflagthosethatpresentsignsoftheillness-andthereforeshouldbeexaminedbyadoctor.Thisensurespeoplewhoneedtreatmentseekit,andallowsdoctorstouseresourcesmoreefficiently.NuritasusesAItoidentifyandextractfromfoodandco-productsoffoodproductioncomponentsthathaveatherapeuticpotential.Epimed:Bybetter aggregatingdata andmakinguseof predictive analytics throughAzureMachine Learning,Epimedishelpingcliniciansdefinethebestcarepathwaysforeachpatient.Inonehealthsystemthishasresultedina21%reductioninHospitalAcquiredInfectionsintheIntensiveCareUnit(ICU)andadropinICUmortalityratesMedicine dose recognition: Dutch hardware startup Mint Solutions developed a scanning technology thatrecognizesmedicinesandallowstoreduceerrorsindosageorkindoftabletsgiventopatients.Thisiscombinedwithananalysisofthehospital’sworkflowandITinfrastructuretoimprovecareandreduceerror.

f. Mobility,transportationandautomotiveRecallMastersusedMLtohelpdealersrecalldefectivecarsfromowners.Dealershipsareinchargeofrecalls,butidentifying the right car and its current owner is sometimes impossible. Recall Masters provides themwithsoftwarethat-byanalysingtransactionsandotherdata-identifiestherightcarandgetintouchwithitsowner.Configuringinterlockingsystemsfornewtrainstations:this iscomplexbecausesomanydifferentpossibilitiesexist.Oursystemautomaticallycreatesanarchitectureforthehardwareandsoftwarethatverifiablycomplieswithallsafetyconditions.Self-drivingcars:AIisalsoaprerequisiteforself-drivingcars.AnumberofexternalkeytechnologiesarerequiredforthedevelopmentanduseofAIandautonomoussystemsthatwillplayafundamentalpartinenablingtheirsuccessful implementation: network infrastructure with minimum latency and high speed connectivitycorrespondingto5G,cloud/edgecomputingforthedistributedprocessingofcomplexcalculations,simulation,certification and testing environments for autonomous systems, Big data analytics. Current human-machineinteractionresearchintheareaofcommunicationandcollaborationaswellassystemsengineeringisequallyimportant.


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g. Onlineservices

‘Personalassistant’servicesrelyonMLforspeechrecognitionandprovidingresponsestousers’questionsandinstructions.A future formofAI assistant, suchas Samsung’sBixby,will be smarterwithmore intuitiveuserinterfaceandcontextawareness.EmailprovidersandbrowserscanrelyonMLtoprotectusersfromspamandmalware.Gmailnowblocksover99.9%ofspam,includingspamthatwasneverseenbefore-andthatthealgorithmsisstillabletoidentify.Translation and speech recognition tools arepoweredbyML. This includes automatic captioning thatmakescontentaccessibletoallusers.ServicesprovidingrankingofresultsandrecommendationsuseMLtofindtheinformationthatismostrelevantinformationfortheirusersandpresentitinthemostusefulway.MLisalsoakeyingredientforimagerecognitionapplications.Collaborative platforms and tools such asMicrosoft Cognitive Services, IBMWatson andBlueMix applicationdevelopment platform, the Bot Framework, and Azure Machine Learning, enables that AI capabilities arebecomingavailabletoeverydeveloperintheworldandencouragingitsusetocreatenewdigitalecosystemsthatcanleadtonewjobs.ThishelpstoaddressconcernsaboutthepowerofAIbeingconcentratedinthepowerofafewlargeU.S.companies.WeshouldconsiderspecialprojectsandhackathonswherethefocusisontheuseofAItoaddressexistingchallengesinlocalcommunities.DigitalplatformssuchasLinkedIn,TaskRabbit,andAmazon’sMechanicalTurkprovideaccesstoawiderangeofopportunitieswithvarying skills.Over time, thisdata canbeused to constructanalyses suchas the LinkedInEconomicGraphtoprovidetransparencyintothesupplyofskillsanddemandsandhowtheyvaryovertimeforagivenregion,especiallyiftheycanbecombinedwithotherdataheldbygovernmentsonlocaldemographicsandbusinesses.


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4. Policychallenges

a. LabourMarketsandProductivityAI/MLandroboticshavethepotentialtoontheonehandimprovemanyaspectsofourprofessionalandpersonallivesandontheothercontributetothecontinuedgrowthofoureconomy.AccordingtoAccenture,AI/MLhasthepotentialtodoubleannualeconomicgrowthratesofdevelopedeconomiesby20351.AI/MLandroboticsareverylikelytoshiftdemandonthelabourmarkettoothertypesofskills.Thisdoesnotmean entire jobs will be wiped out of the market. In the past, such as with Automated Teller Machines,automationactuallyincreaseddemandforhumanlabourbydroppingthecostsforservices.IntheATM-casethenumberoftellersneededinonebranchdecreased,butsinceATMsmadeoperatingabranchcheaperthenumberoftellersemployedoverallactuallygrew.Technology can also contribute to a more efficient labour market at a time where European countries areexperiencingfallingproductivity,anagingworkforce,and increasedglobalcompetition.Location, languageorotherobstaclesmightceasetobeabarrierforworkers.ThereisnosenseindenyingthatSTEM-relatedskillswillbemuchsoughtafter.Still,theadventofAI/MLandroboticswillnotmakenon-technicalprofessions redundant.Humanspecialistswill remainnecessary toworkalongsideandevenguidemachines.Wehaveseensignificantdevelopmentsinhuman/machinecollaborativetechnologyanditsuseintheworkplaceover the last 10 years. Technological advancement will see associates working in close collaboration withmachinestoimprovethecustomer’sexperience,increaseproductivity,andenhancesafety.Innovationandnewtechnologiescontinue tomoveapaceand it is imperative thatanymeasures reflectall typesofcollaborativeoperations.Industrieswillcontinuehiringpeopletoworkwithautomationwhich isaimedatassistingthemtodeliveronspeed,reliability,costandconvenience.Ifwelooktothefuture,oneaptcomparisonistheautomotiveindustry.In the 1980s robotswere introduced and changed the automotive production line, however, thousands andthousandsofpeopleworldwidearestillemployedwithintheautomotiveindustrytoproducecarsFurthermore,tocomeupwithusefulAI/MLorroboticsapplications,agoodunderstandingofhowAIworksanddomainexpertise iscritical.Knowledgeofasectorandagoodsenseoftheproblemstobesolvedare justasimportantasengineeringskills.hereissignificantopportunityforgrowthindevelopingtheinterfacebetweenmachinesandhumans.

1https://www.accenture.com/lv-en/_acnmedia/PDF-33/Accenture-Why-AI-is-the-Future-of-Growth.pdf


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ThefutureofworkwillbeaboutAI/MLhumancollaboration,ratherthanoutrightreplacement.Theideaistouserobotics to empower people and enable them to bemore productive. Jobswill be shared,with some tasksdelegatedtoAIandmonitoredbyhumans.AccordingtoMcKinsey2,veryfewoccupations—lessthan5percent—are candidates for full automation.However, almost every occupationhas partial automationpotential, as aproportionof itsactivitiescouldbeautomated- therepititive tasks-enablingemployees to focusonstrategictasksandpriorities.Ontop,newlaborrolesmayemergewhereexpertswillbeneededtodesign,operate,andmanageintelligentsystems.Thereishowevernodoubtthesetechnologieswillbedisruptive.Tomitigatetherisks,wethinktechnologymustbedeployedinathoughtfulway.Andinthisnotonlyindustry,butalsogovernmentsmustplaytheirparts.Toan increasingextenttheEuropeanUnionand itsMemberStatesshouldstartputtingforwardpoliciesthatleaveallstakeholdersinvolved(employees,employers,…)greaterflexibilitytoadapttotheeverfasterevolutionof the labour market and shift their attention to safeguarding fundamental principles. Such policies will becharacterisedby:

▪ ahighinvolvementofindustryonsupportingexistingbest-practicesandjointinitiatives

▪ promoteSTEMeducationanddigitalskillstrainingandcommittocomputerscience(encouragegirlstoparticipateinSTEMclasses;certificationprogramsforcomputerscienceteachers)

▪ encourage innovative uses of AI – for creation of new jobs through growth of the digital ecosystem,encourageuseofAIinaddressingworkforceandeconomicdevelopmentissues.

▪ promoteeducationandliteracyonAI/MLandroboticsamongtheworkforceandpeoplewithouttechnicaleducation

▪ afarmoreprominentplace/increasedattentionforfosteringculturesofempowerment,responsibilityandpermanenteducationofpeopletoenablethemtobouncebackandadaptmoreeasily

▪ opendebateinaforwardlookingspiritaboutwhatpolicyoptionswillhelpsocietytakefulladvantageofthepotentialopportunitiespresentedbyAI/MLandrobotics

▪ preparefortheconsequencesoftechnologicaldevelopment,byidentifyingandsettingupnewmodelsforenablingsocialsafetynetsinsteadofregulatingtechnologyforfearofrisk

▪ modernizetaxandlabourlawstoconsidertaskworkersandunderstandbettertheneedsoftaskworkers

b. LiabilityAcommonquestioniswhetherweneedtochangetheEUliabilityframeworktocoverAI/MLandrobotics.Weseenoneedtocreatenewrulesforeachnewtechnology,algorithmormathematicalformula.Whenitcomestoindividualrisks,theseshouldbesubjecttothesamenorms,rulesandethicalframeworksthatexistinthedomainswheretheyareapplied.Existinginsuranceschemesandliabilityrulesareabsolutelyfitforthispurpose.

2https://www.researchgate.net/publication/287200513_Mastering_the_Robot_The_Future_of_Work_in_the_Second_Machine_Age


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Liabilityisamorecomplexsubjectwhenwelookatsystemicriskandbroadersocietalimpact.Productsorservicesmightworkasintendedandnotcausedirectharm,butcouldhaveunintendedconsequencesonotherfactors.Howtoaddressthatunintendeddamageisadifficultquestion.Institutions,regulatorsandcourtsmustfindanappropriatebalancebetweenseveralsometimes-competinggoals.Theidealsolutionmustbeonethat:protectsconsumers;ensuresbusinesseshaveanincentivetodesignandprovidesafetechnologiesandproducts;providesflexibilityforbusinessestoinnovate;anddoesnotcreateunnecessaryimpedimentsthatslowinnovation.Technologicalchangeisoftenmetwitharegulatoryorlegislativeresponse.Webelieveitismoreefficienttolookforsolutionswhicharebasedonabroadsocietaldiscussion.GovernmentsandEUinstitutionsshouldactasaconvenorofthedifferentstakeholdersinterestedbythetransformation.Theyshouldstrivetogatherinformationfromdifferentperspectivesandagreeonatrulycollectivesolution.Thismustbeanongoingprocess.AI/MLisinitsveryearlystages,andthecurrentstateoftheartislikelytoevolveinthefuture.Asourunderstandingofthemathematicalmodels increase,wewill becomebetter at assessing the impactof these technologieswhen itcomestoliability.Wethinkanobservatorycouldbehelpfulinthiscase,butonlyifitwastruetoitsname-aplacetocloselyfollowdevelopmentsintechnologytoidentifywhereasocietalchoiceisneeded.

c. ResponsibletechnologydevelopmentAswithanytechnology,itisimportanttomaximizethepositivesandminimizeconcreteharms.Therapidadvanceofthesetechnologiesraisedconcernsoversafetyandfairness.Technologicaldevelopmenthasbeenfastbutitisnotanuncontrollableforce.Wehavetheabilityandtheresponsibilitytoshapethewaythesetoolsareapplied,in order tomaximize thebenefits of this technology for everyone. Still, no system is perfect, and errorswillemerge.Wemustbereadytoidentifyandcorrectthem.With growing penetration ofmachines into our lives, ethical questionswill emerge in growing numbers andsignificance.Withaconstantlyevolvingsociety,theethicalchallengesweareconfrontedwithwillalsocontinuetochange.InthatlightitisobviousthathumanjudgmentmustandwillremainakeycomponentofAI/MLandroboticsapplications.Theethicalprinciplestobeappliedbythesetechnologiesinthefuturewill,toalargeextent,bedeterminedbythepeopleresearchinganddevelopingthemtoday.Withaviewonreducingbiasandembeddingtheprinciplesofpluralistsocietiesandhumanvaluesinthesetechnologiesweshouldontheonehande.g.encouragepeoplefromavarietyofbackgrounds todevelopan interest in computer sciences. On theotherhand,advances intechnical capabilities e.g. will strengthen the ability to meet the increasingly complex challenge. Rigorousengineeringresearchcanprovidethedevelopersofthesetechnologieswithapproachesandtoolstheycanusetotackletheseproblems.In summary, realizing the fullpotentialofAI/MLand robotics,webelieve,will requirecollaborationbetweenindustry,governments,civilsocietyandtheresearchcommunitytoshapeafutureinwhichpeoplefromavarietyofbackgroundsandfrommultipledisciplinesworktogether.


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CASESTUDIESFutureofLifeInstituteTheFLIcatalyzesandsupportresearchandinitiativesforsafeguardinglifeanddevelopingoptimisticvisionsofthe future, including positive ways for humanity to steer its own course considering new technologies andchallenges. In 2015 FLI published anopen letter onAI setting out the an approach to avoid discrimination /researchprioritieshttps://futureoflife.org/ai-open-letter/.TheLeverhulmeCentrefortheFutureofIntelligenceTheCentrespansinstitutions,aswellasdisciplines.ItisacollaborationledbytheUniversityofCambridgewithlinks to theOxfordMartinSchoolat theUniversityofOxford, ImperialCollegeLondon,and theUniversityofCalifornia, Berkeley. It is supported by Cambridge’s Centre for Research in the Arts, Social Sciences andHumanities(CRASSH).AsProfessorPriceputit,“aproposalthisambitious,combiningsomeofthebestmindsacross four universities and many disciplines, could not have been achieved without CRASSH’s vision andexpertise.”PartnershiponAIInSeptember2016,Amazon,DeepMind/Google,Facebook,IBM,AppleandMicrosoftformedthePartnershiponAI.Itsmissionisto“studyandformulatebestpracticesonAItechnologies,toadvancethepublic’sunderstandingofAI,andtoserveasanopenplatformfordiscussionandengagement.”Themajorityofvotesintheboardgoesto research institutions and other non-private groups, to limit the influence of private interests. While thecorporatemembersarecurrentlymostlyUScompanies,mostoftheresearchersandscientistsrepresentingthemareEuropean.B20recommendationsonartificialintelligenceAmulti-stakeholderprocessaheadoftheG20resultedinapolicypaperondigitisationaddressingAI.Itincludesa series of recommendations to embrace the technology, including anOECDmulti-stakeholder initiative and“adjusting regulations to facilitate the use of emerging technologies, such as big data, and foster relatedinvestmentandinnovation”.


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d. TransparencyandtrustIndustryandpolicy-makerssharethesameobjective -ensureusersarecomfortablewithandunderstandthewaytechnologyworks. Thechoicesofhowbest toattain thisobjective shouldhingeon the specificsofeachuse casedependingalsoontheunderlyingtechnologies.Aone-size-fits-alltransparencyobligationdoesn’tseemtobeafeasibleoption. The fast-paced development of these technologies requires a flexible approach, allowing the industry torespondtoevolvingconsumers’needs.AI/ML and robotics applications are data-heavy. This naturally raises concerns about the potential implication ofprivacyrules.Totheextentthatpersonaldataarecollectedandprocessedandindividuals’privacyisimpactedinAIandroboticsapplications,therecentlyupdatedEUdataprotectionframeworkwiththeadoptionoftheGDPRapplies.Thisframeworkistechnologicallyneutralandwasreviewedpreciselywiththeaimtocovertechnologyadvancement.In particular, the GDPR provisions on automated individual decision-makingwill be relevant in certain AI/ML androboticsenvironmentaswellastheprovisionsthatrelatetotransparencyandtherighttoinformation.ToreapthesocietalbenefitsofAIsystems,weasmembersofsocietyfirstneedtotrustthem.Therightleveloftrustwillbeearnedthroughrepeatedexperience,inthesamewaywelearntotrustthatanATMwillregisteradeposit,orthataroadvehiclewillstopwhenthebrakeisapplied.Putsimply,wetrustthingsthatbehaveasweexpectthemto.Trustisbuiltuponaccountability.Wemustensurevisibilityintohowautomateddecisionsaretakentomitigaterisksofbias,discriminationsandharm.Public institutionsandtheprivatesectorsharethecommonobjectiveofmakinginformation available in a user-friendly and user-understandableway. But simply providing the information is notalwaysthebestsolution.InthecaseofAI/MLandrobotics,itseemsunlikelypeoplewouldextractanymeaningfromtechnicaldescriptionsofcomplexmathematicalmodels.Webelieveaccountabilitywillbebestachievedbymakingsystems interpretable and auditable. Users should be able to interpret the outcomes of an AI-based process andevaluatehowsuchoutcomeswerereached.Focusingontheoutcomeandnotonthemechanicsoftheprocessseemstobethemostappropriatewaytoensuretechnologyissafe,ethicalandfair.OneoftheprimaryreasonsforincludingalgorithmicaccountabilityinanyAIsystemistomanagethepotentialforbiasin the decision-making process. This is an important and valid concern among those familiarwith AI. Bias can beintroducedbothinthedatasetsthatareusedtotrainanAIsystem,andbythealgorithmsthatprocessthatdata.BiasesofAIsystemscannotonlybemanaged,butalsothatAIsystemsthemselvescanhelpeliminatemanyofthebiasesthatalreadyexistinhumandecision-makingmodelstoday.AIsystemsshouldfunctionaccordingtovaluesthatarealignedtothoseofhumans,sothattheyareacceptedbyoursocietiesandby theenvironment inwhich theyare intended to function.This isessentialnot just inautonomoussystems, but also in systems based on human-machine collaboration, since valuemisalignment could preclude orimpedeeffectiveteamwork.Itisnotyetclearwhatvaluesmachinesshoulduse,andhowtoembedthesevaluesintothem.Severalethicaltheories,definedforhumans,arebeingconsidered(deontic,consequentialist,virtue,etc.)aswellastheimplicationsoftheirusewithinamachine,inordertofindthebestwaytodefineandadaptvaluesfromhumanstomachines.In industries like healthcare and finance, the relevant professional ethical principles are explicitly encoded andpracticedbyprofessionalsinthefieldalready.InAIsystemsdesignedtohelpprofessionalsinthesedomains,thesebestpracticesandprinciplescouldformthecoreoftheethicsmoduleforsuchsystems.Ethicsmodules,however,shouldbeconstantlyadaptedtoreflecthumans’bestpracticesintheireverydayprofession.


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Theuseofalgorithmsindecision-makingshouldbegroundedinacoresetofprinciplestoensurethattechnologyisbuiltwithintelligencethatistransparentandsecure,andthatsetsthehighestbarforprivacyprotections,whilealsobeinginclusiveandrespectfulofall.CASESTUDIESEuropeanandglobaliniativesonEthicsandMachineLearningappliedtoidentifyandcorrectbiasAIsystemscanhelpeliminatemanyofthebiasesthatalreadyexistinhumandecision-makingmodelstoday.Companiesareworkingwithcivilsocietygroupsandotherstoidentifytherisksandstepstobetakentomitigatethem.Thereisalsopotentialtousethesesystemstohelpmitigatebias:forinstance,theGeenaDavisInstitutecreatedamachinelearningtoolwithGoogletoeasilymeasuregenderrepresentationinmovies.AttheglobalNeuralInformationProcessingSystems(NIPS)conferenceinBarcelonalastyear,aresearchteamlaidoutanapproachforavoidingbiasinalgorthims.PrivacyandtransparencyinstrumentsGoogleoffersanumberoftoolstowebmastersanduserstohavevisibilityonhowtheservicestheyusework.Forexample,theHowSearchWorkspageandWebmastersGuidelinesshedlightontheinnerworkingsofsearch.Madepublicareguidelinesforitssearchqualityevaluators-realpeoplewhoassessthequalityofsearchresults.Users have full visibility and control on how their information is used in different applications throughMyAccount.DigitalEthicsLab/EuropeanEthicalCodeforDataDonationConveningtherelevantstakeholderstotackleissuessuchas“TheEthicsofMedicalData&AdvancedAnalytics”isessentialifwearetounlockthevastpotentialofdata,whichisfastbecomingamostvaluableresource.Withinthe Oxford Internet Institute at the University of Oxford newly established Digital Ethics Lab, there is anopportunitytocollaborateonnewprojectssuchastheonesupportedbyMicrosoftondevelopingaEuropeanethicalcodefordatadonationandencouragingdataphilanthropy.Thegoalistoexplorewaysinwhichcitizenparticipationinresearcheffortsmaybesupportedvia‘datadonations’,andtoshapebestpracticewithregardsto respecting individuals’ rights as well as ensuring proper regulatory oversight of existing and future dataexchangepartnershipsbetweengovernmentsandtechcompanies.TheIEEEGlobalInitiativeforEthicalConsiderationsinArtificialIntelligenceandAutonomousSystemsThemissionofthisinitiativeis“Toensureeverytechnologistiseducated,trained,andempoweredtoprioritizeethical considerations in the design and development of autonomous and intelligent systems.” The firstdeliverable of this effort -Ethically AlignedDesign - gathers the collective input of over one hundred globalthoughtleadersinthefieldsofAI,lawandethics,philosophy,andpolicyfromtherealmsofacademia,science,and the government and corporate sectors. It identifies issues and candidate Recommendations in fieldscomprisingAIandAutonomousSystems.


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5. Whatcanpolicy-makersdo?

● FacilitatefactbasedstakeholderdialoguePolicies should be grounded on current state of the art and research within a foreseeable timescale.DIGITALEUROPEisreadytoidentifyandprovideanynecessaryinput.

● EnsureflexibilityandprotectinnovationManyexcitingnewAIbasedapproachesandtechnologiesarestillnascent.Weencourageacautiousandflexiblepolicyapproachthatwillallowinnovativeusestoflourishandreachtheirfullpotential.Webelieveconsensus-drivenandself-regulatoryprocessesarethebestwayforwardandshouldbeencouraged.

● Createchannelsformulti-stakeholderdialogueItisoftheutmostimportancetoensureacontinuousexchangeamongabroadrangeofstakeholders,includingthe privacy and public sectors, the academic community, NGOs and civil society. This effort should not beconfinedwithinnationalborders-anEUwideandinternationalapproachisneeded,asthesearetrulyglobaltechnologies.Thismayalsoincludediscussionaboutstandardsandcertification,includingpossiblegapanalysis,whenappropriateandprovidedtheindustryisinvolvedinthediscussionfromtheoutset.

● Promotedata-driveninnovationandfreedataflowsThesetechnologiesrelyondatatobeusefulanddeliverprogressivelybetterresults.TheEUandMemberStatesshouldcreateanenvironmentthatisconducivetofreeflowofdatainallsectorsofsocietyandtheeconomy.Publicinstitutionsshouldencouragethecreationandsharingofhigh-qualitydatasets.

● SupportresearchandacademicinstitutionsPublic institutions should be a driving force in promoting research into the novel application of thesetechnologies, to ensure they meet social challenges and address potential limits and shortfalls. Supportinginternational exchange should constitute a big part of this effort. In order to secure the technological edge,achievelong-termtechnologyleadership,providedecentworkingconditionsandrapidly leveragecompetitiveadvantages, innovation labs/hubs for AI and autonomous systems should be established to support projectsgearedtowardsimplementationandtechnologymigrationtotheindustrialenvironment.


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● Buildabiggerandmorediversetechcommunity

GovernmentsshouldencourageaccesstoSTEMeducationandcareers.Theyshouldtakediversityveryseriously,toensureAI/MLandroboticsaredevelopedinanunbiasedandinclusiveway:

- Government agencies, associations and industrial enterprises should also be encouraged tomakeanonymisedbigdataavailablefortrainingofmachinelearningsystems.

- FundresearchintoautomatedactionplanninginautonomoussystemswithfocusontherobustnessofcurrentAImethodsinreal-lifescenarios

- Supporteddevelopmentofstandardizedinterfacesandreferencearchitecturemodels,togetherwithcomprehensive,reliableandcertifiedknowledgebases.

● Identifythechallengesforemployment,trainingandprofessionaldevelopmentpolicyEvaluatepublicpolicyapproachestomitigateanyimpactonjobsandtopromotetheinclusionofallcitizensinreapingthebenefitsofAI.Identifytherequiredskillsetsanddeveloproadmapsforattainingthenecessaryskillsbasedonindustrialusecases.


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ANNEX–Listofrelevantarticlesandreports

1. McKinseyGlobalInstitutestudyonAutomationanditsimpactontheeconomy(Jan2017)

2. LisbonCouncildiscussionpaper-AI/MachineLearning:Opportunities+Challenges(Nov2016)

3. ITI-“ITIDecodes:ArtificialIntelligence”(Oct2016)

4. CenterforDataInnovation-“ThePromiseofArtificialIntelligence”(Oct2016)

5. MITTechReview-“AretheExpertsWorriedabouttheExistentialRiskofArtificalIntelligence?”(Sept2016)

6. Forbes-“ArtificialIntelligenceIsHelpingDoctorsFindBreastCancerRisk30TimesFaster”(Aug2016)

7. NewYorker-“TheHype-andHope-ofArtificialIntelligence”(August2016)

8. WallStreetJournal-“TheRobotsareComing.WelcomeThem”(August2016)

9. MorningConsult-“SkynetIsNotComing:TheMythsandRealitiesofArtificialIntelligence”(August2016)

10. CenterforDataInnovation-“ArtificialIntelligenceIsKeyToUsingDataForSocialGood”(July2016)

11. ITIF-“It’sGoingToKillUs!-AndOtherMythsAbouttheFutureofAI”(June2016)

12. OECD-TheRiskofAutomationforJobsinOECDCountries(May2016)

13. AnalysisGroup-“TheGlobalEconomicImpactsAssociatedwithArtificialIntelligence“(Feb2016)

14. Netherland'sScientificCouncil forGovernmentPolicy -Mastering theRobot;TheFutureofWork in theSecondMachineAge(Dec2015)

15. AnothergreatresourcetopointtoismappingofEuropeanAIstartups,comingfromAnextensivelistofEuropeanAItechstartupstowatchin2017

16. PWC analysis on the UK (labor) market http://www.pwc.co.uk/services/economics-policy/insights/uk-economic-outlook.html

17. ReportonMLfromTheRoyalSociety

18. IoE,UnderstandingtheFutureofWork(March2017)

19. http://www.juridicum.su.se/user/masc/analyzethis.pdf

20. Accenture:WhyArtificialIntelligenceistheFutureofGrowth

--Formoreinformationpleasecontact:DamirFilipovic,DIGITALEUROPE’[email protected]


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