藝術(shù)的可能性 人工智能 和區(qū)塊鏈

ArtofthepossibleartificialIntelligence&Blockchain

Contents

03Introduction

04ArtificialIntelligence:Anexplanation

05SymbolicAI-1955

05MachineLearning-1995

08DeepLearning-2011

10AIinFinance

11PersonalisedFinancialPlanning

12FraudDetetctionandAnti-Money

Laundering

13ProcessAutomationandEfficiency

14RisksofAI

14Howdidyoudecidethat?

14Ethics,ResponsibilityandStewardship

15Whataboutmydata?

17Blockchain:AnExplanation

17BriefHistory

Sowhatisit?

That’sgreat,buthowdoesitwork?

Sowhereisallthemoney?

19Don’tforgetaboutDLT

BlockchaininFinance

RisksofBlockchain

TheOpportunityforNHSFinance

Blockchain

23E-Invoicing

AI

24DrivingincreasinglyefficientNHSfinancesystemsandprocess

24PLICS&predictiveandprescriptiveanalysis

25Reportingtofinanceandnon-finance

26ClosingStatement

27Resources

28Bibliography

Introduction

ARTOFTHEPOSSIBLEARTIFICIALINTELLIGENCE&BLOCKCHAIN|3

Fintechisthefinancialtechnologyandinnovationthataimstocompetewithtraditionalfinancialmethodsinthedeliveryoffinancialservices.Itisanemergingindustrythatusestechnologytoimproveactivitiesinfinance.ThereisasignificantamountofvariationintheperformanceandefficiencyofexistingNationalHealthService(NHS)Financesystemsandprocesses,andthecombinationofalargeandhighlyskilledfinancialworkforcewithstate-of-the-artemergingtechnologiespresentsNHSFinancewithanopportunitytoaddresssomeofthesedisparities.

Thefirstchallengehoweverissimplytounderstandsomeofthesetechnologies.Thisbriefingwillevolvetocontainmanytechnologiesastheyemergebutstartsoutwithtwocomplextechnologieswhicharenotwidelyunderstood.However,overthenextdecadeifimplementedsecurely,safelyandcorrectly,theymayofferimprovementstotheNHSandgiveNHSFinanceanopportunitytodirectlycontributetothedeliveryofbettervaluehealthandcareforentirepatientpathways.

ThisbriefingaimstoprovideashorthistoryandabasiclevelofunderstandingofArtificialIntelligenceandBlockchaintechnologies.Itwillalsoidentifyexistingimplementationsinafinancesettingandproposeareasofopportunity,sotogetherwecanbegintothinkaboutwhatplacethesetechnologiescouldhaveinthefutureofNHSFinance.

4|ARTOFTHEPOSSIBLEARTIFICIALINTELLIGENCE&BLOCKCHAIN

ArtificialIntelligence:Anexplanation

In1950AlanMathisonTuringpublishedaground-breakingpaperwhichposedthequestion“Canmachinesthink?”1

FiveyearslaterProfessorJohnMcCarthycoinedtheterm“artificialintelligence”(AI)whichoriginallyproposeda

2-monthstudytoinvestigatetheassumptionthat:

“Everyaspectoflearningoranyotherfeatureofintelligencecaninprinciplebesopreciselydescribedthatamachinecanbemadetosimulateit.Anattemptwillbemadetofindhowtomakemachinesuselanguage,formabstractionsandconcepts,solvekindsofproblemsnowreservedforhumans,andimprovethemselves.”2

62yearsonfromMcCarthy’s(largelytheoretical)study,ProfessorDameWendyHallandJér?mePesentiina2017papercommissionedbytheUKgovernmentstatesthat“AIcouldcontribute￡630BilliontotheUKeconomyby2035”3.

InthisbriefingwewilllookathowAIhasevolvedoverthepastsixdecades,whattoday’sArtificialIntelligenceimplementationslooklike,andtheopportunitiesAIcouldpresenttoNHSFinance.

ComputingMachineryandIntelligence,1950

AProposalfortheDartmouthSummerResearchProjectonArtificialIntelligence,1955

GrowingtheartificialintelligenceindustryintheUK.,2017

ARTOFTHEPOSSIBLEARTIFICIALINTELLIGENCE&BLOCKCHAIN|5

Symbolicai-1955

Duetoalackofavailablecomputingpowerandelectronicdata,in1955AIwasprimarilymathematicalandthereforesufferedfromanimmediatelackofinitialinvestment.Forthefirst15yearsthetechnologyexperienceditsfirstAI

Winteruntilthe1980swhentheUS,Japan,andtheUKbegancompetingheavilyinAIinvestmentforthefirsttime.

AIWINTER

AIwinterisaquietperiodforartificialintelligenceresearchanddevelopment.Overtheyears,fundingforAIinitiativeshasgonethroughanumberofactiveandinactivecycles.Thelabel“winter”isusedtodescribedormantperiodswhencustomerinterestinartificialintelligencedeclines.

ThissawthefirstworkingimplementationsofearlyAIintheformof‘ExpertSystems’4whichwereusedtovaryingdegreesofsuccessforintegratedpersonalfinancialplanningandmorenotably‘programtrading’onthestockmarket.Thelatter

wasthoughttohavecontributedinparttothetroublesomemarketswingsofthelate1980sand‘BlackMonday’whichsawa508-pointdropintheDowJonesIndustrialAverage(DJIA)whichwasthelargestone-daydropintheindex’shistory.Thoughthesesystemstendedtobecomemoreaccurateasmoreruleswereadded,thesesystemswereexpensivetoscale,labourintensive,andrequiredsignificantupkeep.Theyalsooftenrespondedpoorlytocomplexsituationswheretheformalrulesuponwhichtheygeneratedtheirinferenceswerenotflexibleenoughandlackedsafeguardsorappropriateexceptionhandling.5

AtthistimepersonalcomputerswereintroducedbyIBMandAppleandthemuchhardertomaintain‘ExpertSystems’felloutoffavour.Fundingdriedupandthecompaniesthemselvesbegantodefaultontheirpromises.Thisledto

asecondAIwinteruntil1993whenTheFinCENArtificialIntelligenceSystem(FAIS)wassuccessfullyimplementedandinitsfirsttwoyearsreviewedover200,000transactionsaweekidentifyingmoneylaunderingbreachesworthapproximately$1BillionUSD.6Thiswasoneofthefirst

consistent,scaledandfinanciallysuccessfuldemonstrationsofAIandledthetechnologyoutofitssecondwinterandintoanewageofpracticaltechnologicaldevelopment.

Machinelearning-1995

Machinelearning(ML)wasatermcoinedbyArthurSamuelofIBMin19597.MLisasubsetofAIandreferstothedevelopmentofdigitalsystemsthatimprovetheirownperformanceonagiventaskovertimethroughexperience.

MListhemostwidelyusedformofAI,andhascontributedtoinnovationslikeself-drivingcarsanddatamining.

MLexperiencedsignificantpracticaladvancesinthe90sdueto:

improvementstoalgorithms

increasesinfunding

hugegrowthintheamountofdatacreatedandstoredbydigitalsystems

theabilitytoaccessthisdataviatheinternet

increasedaccesstospecialistcomputinghardwaresuchasGraphicalProcessingUnits(GPUs)andcloudcomputing.

PROTRADER:AHuman-aidedLearningSystemfor,1987

Explainingdecisionsmadewithartificialintelligence,n.d.

ArtificialIntelligenceinFinance,2019

SomeStudiesinMachineLearningusingthegameofcheckers,1959

MLhasmanymovingpartssoforthepurposesofthisbriefingwearejustgoingtolookatthelearningapproachesandthelearningprocess.Atthisjunctureitisimportanttostatethedifferencebetweenanalgorithmandamodel.

Analgorithmisaformulaorsetofrules(orprocedure,processes,orinstructions)forsolvingaproblemorforperformingatask.InArtificialIntelligence,thealgorithmtellsthemachinehowtofindanswerstoaquestionorsolutionstoaproblem.8

Amodelistheoutputofamachinelearningalgorithmwhichhasbeenrunondata.Amodelrepresentswhatwaslearnedbyanalgorithm.9

LearningApproaches

Learningapproachesarethedifferentwaysinwhichanalgorithminteractswithanewdatasettolearnfromit.Therearemanylearningapproaches,butforthepurposesofthisbriefingwewillcoverthethreemainones:

SupervisedLearningisataskwhichallowsanAImodeltolearnfromlabelleddata.

Inhealthcare,scansthatarelabelledashealthyandcancerousbeingusedtotrainamodelsothatunlabelledimagescanbeidentifiedashealthyorcancerous.

Unsupervisedlearningisatypeofmachinelearningalgorithmusedtodrawinterpretationsfromsetsofdatawithoutanylabelledresponses.Itcanbeusedtofindpatternsorclusterswithinthedata.

Anexampleofunsupervisedlearningisclustering.Sortingpeopleintogroupsbasedonfeaturesbutnotlabellingthegroups.

ReinforcementLearningisatypeofdynamicprogrammingthattrainsalgorithmsusingasystemofrewardandpunishment.Thealgorithmisexposedtoatotalrandomandnewdataset,anditautomaticallyfindspatternsandrelationshipsinsideofthatdataset.Themodelisrewardedwhenitfindsa‘desired’relationship,butitisalsopunishedwhenitfindsan‘undesired’relationship.8

Itissimilartothestructureofhowweplayavideogame,inwhichthecharacter(algorithm)engagesinaseriesoftrials(challenges)togetthehighestscorepossible(reward).”

Thealgorithmlearnswithoutinterventionfromahumanbymaximisingitsrewardandminimisingitspenalty.Atfirstthecharactermaymoverandomly,buttheinformationfrompreviousrunsisstoredandusedtoimprovefutureattempts.

6|ARTOFTHEPOSSIBLEARTIFICIALINTELLIGENCE&BLOCKCHAIN

Glossary-ArtificialIntelligenceinMedicalImaging,2019

DifferenceBetweenAlgorithmandModelinMachineLearning,2020

ARTOFTHEPOSSIBLEARTIFICIALINTELLIGENCE&BLOCKCHAIN|7

EXAMPLESOFALGORITHMS

Logisticregressionisakindofstatisticalanalysisthatisusedtopredicttheoutcomeofadependentvariablebasedonpriorobservations.Forexample,analgorithmcould

determinethewinnerofapresidentialelectionbasedonpastelectionresultsandeconomicdata.

Linearregressionisakindofstatisticalanalysisthatattemptstoshowarelationshipbetweentwovariables.Linearregressionlooksatvariousdatapointsandplotsatrendline.Linearregressioncancreateapredictivemodelonseeminglyrandomdataandhighlighttrends.

Adecisiontreeusesatree-likegraphormodelasastructuretoperformdecisionanalysis.Ituseseachnodetorepresentatestonanattribute,eachbranchtorepresenttheoutcomeofthetest,

RandomForestsareensemblelearningmethodsfordataclassificationandregression.Theyconstructamultitudeofdecisiontreesduringthetrainingandoutputtheclassthatisthemodeoftheclasses(classification)ormeanprediction(regression)oftheindividualtrees.

Learningprocess

WhicheverlearningapproachisusedeachMLsystemcomprisesthefollowingcomponentswhichformthelearningprocess:

Aproblemincludesdefinitionsandformulationsontheproblemscharacteristics

Datasourceincludesdefinitionsandformulationsonthedata’scharacteristics

Algorithmselection(Seeexamplesofalgorithms)8

Optimisationmethodsrangesignificantlyandinclude,FirstOrder,Highorder,Derivativefreeandpreconditioningoptimisationmethods–Seethisreferenceforexamples10

ValidationandverificationValidationischeckingthatthesystemmeetstheusersneeds(Arewebuildingtherightsystem)andverificationchecksthatweachievesthespecification(arewebuildingtheproductright.

ThisexplanationofMachineLearningmayappeartobeabittooinvolvedbutunderstandingthebuildingblockscanhelpustobetterunderstandtheopportunitiesthatexistbeyondcomparativelymechanicalRoboticProcessAutomation(RPA)implementations,whichwithinthenext5yearscouldbecomecommonplaceinNHSFinancesystemsandprocesses.

MLdevelopmentcontinuestodayandisstillthemostwidelyusedsubsetofAI,butaswemovefurtheralongourtimelinetowardstheendof2010furtherimprovementsincomputingpoweranddatastorageleadtothepracticalemergenceofanothersubsetofAI.

ASurveyofOptimizationMethodsfrom,2019

8|ARTOFTHEPOSSIBLEARTIFICIALINTELLIGENCE&BLOCKCHAIN

DeepLearning-2011

ForalongtimeMLrequiredagooddealofmanualfeatureengineering,i.e.lookingatdatatoextractfeatureswhichwereimportantandmeaningfulforaproblem.Oncethesefeatureswereidentifiedasimplemodelcouldbeused.

Byincreasingthecomplexityofthemodels,throughtheintroductionofmanymorelayers,itwasfoundthatthese‘deep’modelscouldbetaughttolearnthesefeatureswithouthumaninteraction,reducinghumaneffortandhencereducedcost.

Inadditionitwasfoundthatthesedeepmodelswereabletoidentifycomplexfeatureswhichwerenoteasilyfoundbyhumaninspection.

Itwasthiscombinationofreducedcostandincreasedperformancethatleadtotheriseofdeeplearning.

Deeplearning(DL)isviewedasabranchofMLandassuch,isalsoregardedasasubsetofAI.

By2011,computerprocessingpowerandstorageofbigdataweregrowingataformidablerateandthiscombinationbroughtanewageofsupercomputerwhichmeantthatpracticalscaledworkonArtificialNeuralNetworks(ANNs)becamepossible.

TheconceptoftheseNeuralNetworkswasfirstauthoredbyMcCulloch&Pitts11ina1943paperwhichconsideredthehumanbrain’sbiologicalneuralsystemasanappropriateandefficientprocessorbyusinglayersof‘neurons’toperformdifferentlogicfunctionssimultaneously.Thatistosaythatourbiologicalneuralnetwork(thehumanbrain)makesconnections(firingneurons)basedonourperceptionsandoutsidestimulus.

ANNstrytomakecomputersmimicthecapabilitiesofthehumanbraininthewayitprocessesinformation.ANNsaredesignedtoreplicatethefunctionsoftheneuralstructureofthehumanbrain.Theyareadaptive,flexible,adjustingandlearningwithnumerousanddiverseexternaland

internalstimulus.12

‘deep’modelscouldbetaughttolearnthesefeatureswithouthumaninteraction

Alogicalcalculusoftheideasimmanentinnervousactivity,1943

NeuralNetworks,ArtificialIntelligenceandtheComputationalBrain,2020

ARTOFTHEPOSSIBLEARTIFICIALINTELLIGENCE&BLOCKCHAIN|9

NeuralWhat?!Saythatagainplease

Anextremelyoversimplifiedexamplecouldbethepainweexperiencefromgettingburnedbyfire.Whenthishappensforthefirsttime,aconnectionismadeinyourbrainthatidentifiesthesensoryinformationknownasfire(flames,smellofsmoke,heat)andrelatesitwithpain.Thisishowwelearn,ataveryyoungage,tonotgetburned.Throughthissameneuralnetwork,wecandoalotofgenerallearninglike“icecreamtastesgood”.Theselessonsarenotalwayscorrect(thereisbadicecream!),butassumptionsarecorrectedthroughmoreexperiences,whichallowshumanstheprivilegeofadaptivelearningandtobecomegeneralists.

PracticallyDLisastatisticaltechniqueforfindingpatternsinlargeamountsofdata.Generallyspeaking,theblueprintsofaDLalgorithmareANNs.JustlikeinMLtheseDLalgorithmscanbeusedwiththesupervised,unsupervised,orreinforcementlearningapproachesmentionedearlier.

Althoughdeeplearningsolutionsdoproduceimpressiveresults,theyarenotabletorivaltheintricatecomplexityofthehumanbrain.

ThereareunsurprisinglymanymoreareasofAI,MLandDLsuchas;LongShortTermMemoryNetworks(LSTM),

ConvolutionalNeuralNetworks(CNN),LowShotLearning(LSL)etc.ButthisbriefingseekstoprovideaprimitiveunderstandingofAI.

Ifyouarekeenforadeeperdive,pleasehavealookattheBibliography.Alternatively,ifyoupreferamoreaccessibledemonstrationofaDeepLearningNeuralNetworkyoucanwatchtheaward-winningdocumentaryAlphaGoonYouTube.

ALPHAGO

AlphaGoisthefirstcomputerprogramthatdefeatedaprofessionalplayerontheboardgameGoinOctober2015.LaterinOctober2017,AlphaGo’steamreleaseditsnewversionnamedAlphaGoZerowhichisstrongerthananyprevioushuman-championdefeatingversions.Goisplayedon19by19boardwhichallowsfor10171possiblelayouts(chess1050configurations).Itisestimatedthatthereare1080atomsintheuniverse.

ClickheretowatchtheAlphaGodocumentary

10|ARTOFTHEPOSSIBLEARTIFICIALINTELLIGENCE&BLOCKCHAIN

Aiinfinance

TodaytheUKgovernmentdefinesAIas:

“Theuseofdigitaltechnologytocreatesystemscapableofperformingtaskscommonlythoughttorequireintelligence.AIisconstantlyevolving,butgenerallyit:

involvesmachinesusingstatisticstofindpatternsinlargeamountsofdata

istheabilitytoperformrepetitivetaskswithdatawithouttheneedforconstanthumanguidance”13

InresponsetothepaperbyHallandPesenti3mentionedatthebeginningofthisbriefingthegovernmentpublishedanIndustrialStrategyandplacedArtificialIntelligenceandDataasoneof4GrandChallenges,whichisnowsupportedbythe

￡950mAISectorDeal.14

ThegovernmentalsosetupthreenewbodiestosupporttheuseofAI:

AICouncilwhichisanexpertcommitteeprovidinghigh-levelleadershipindeliveringtheAISectorDeal.

OfficeforAIwhichworkswithindustry,academiaandthethirdsectortocoordinateandoverseetheimplementationoftheUK’sAIstrategy

CentreforDataEthicsandInnovationwhichidentifiesthemeasuresneededtomakesurethedevelopmentofAIissafe,ethicalandinnovative.

InDecember2018afollow-uppaperForgingourFuture15

statedthatthefirstmissionoftheGrandChallengewasto:

“Usedata,ArtificialIntelligenceandinnovationtotransformtheprevention,earlydiagnosisandtreatmentofchronicdiseasesby2030.”

Inthefinancialservicesindustry,existingAIapplicationsincludealgorithmictrading,portfoliocompositionandoptimisation,modelvalidation,backtesting,robo-advising,virtualcustomerassistants,marketimpactanalysis,regulatorycompliance,andstresstesting.

IntheiroriginalpaperHallandPesenti3identifiedthreeareasoffinanceintheUKwhereAIhasgreatpotential:

personalisedfinancialplanning

frauddetectionandanti-moneylaundering

processautomationandefficiency

Aguidetousingartificialintelligenceinthepublicsector,2019

IndustrialStrategy,BuildingBritainfitforthefuture,2017

Government,ForgingourFuture:IndustrialStrategy-thestorysofar,2018

ARTOFTHEPOSSIBLEARTIFICIALINTELLIGENCE&BLOCKCHAIN|11

INTERNETOFTHINGS(IOT)

TheIOTistheinterconnectionviatheinternetofcomputingdevicesembeddedineverydayobjects,enablingthemtosendandreceivedata.Examplesof‘things’thatare‘smart’includeAmazonsRing

doorbell,SamsungsmartFridges,theiKettleorPhillips

Huesmartlights

Personalisedfinancialplanning

Whilehumanfinancialadviceiscostlyandtime-consuming,AIdevelopmentssuchasrobo-advicehavemadeitpossibletodevelopcustomisedinvestmentsolutionsformostofitscustomers,untilrecentlythisbespokefinancialadvicewasreservedforaverysmallminorityofwealthyindividuals.

Inthebankingsector,AIpowersthesmartchatbotsthatprovideclientswithself-helpsolutionswhilereducingthecall-centres’workload.Anumberofappsofferpersonalisedfinancialadviceandhelpindividualsachievetheirfinancialgoals,bytrackingincome,essentialrecurringexpenses,andspendinghabitsandconstantlyadapttocomeupwithanoptimisedpersonalplanforeachuser.

ThebiggestUSbanks,havelaunchedmobilebankingappsthatprovideclientswithreminderstopaybills,plantheirexpensesandinteractwiththeirbankinaneasierandmorestreamlinedway,fromgettinginformationto

completingtransactions.16

Voice-controlledvirtualassistantspoweredbytheInternetofThings(IOT)deviceslikeAmazon’sAlexaarealsogainingtractionfast.Thepresenceofself-educatingIOTdeviceswillcontinuetoscale,anduserswillbeabletocheckbalances,

schedulepaymentsandreceivefinancialadviceinthecomfortoftheirownhomewithoutleavingthesofaorpickingup

theirphone.

“Usedata,ArtificialIntelligenceandinnovationtotransform

theprevention,earlydiagnosisandtreatmentofchronicdiseasesby2030.”

TheGrowingImpactofAIinFinancialServices,2019

12|ARTOFTHEPOSSIBLEARTIFICIALINTELLIGENCE&BLOCKCHAIN

LEGALPROFESSIONALPRIVILEGE(LPP)ROBOT

Legalprofessionalprivilegeprotectsallcommunicationsbetweenaprofessionallegaladviserandtheadvisor’sclientsfrombeingdisclosedwithoutthepermissionoftheclient.TheRobotreducesthetimetakeninsuchreviewsbyover80%andgreatlyreducesthelevelofmanpowerneededtoconductthetask.Accuracylevelsarealsoimproved.

Frauddetectionandanti-moneylaundering

FrauddetectionwasoneoftheearliestsuccessfulimplementationsofAIbackin1993withtheFinCENArtificialIntelligencesystemmentionedearlier.InthedecadessincethefraudandmoneylaunderingsectorhasseenoneofthemostsignificantincreasesinAIadoption.

InatypicalyeartheUKSeriousFraudOffice(SFO)processesover100milliondocumentsinfraudandcorruptioncases.

TheSFOusedtheRAVNroboticsystemwhichisreferredtoasaLegalProfessionalPrivilege(LPP)robot.RAVNsiftsdocumentsinto“privileged”versus“non-privileged”piles,

indexesandcompilessummaries.IntheRolls-Roycebribery2017case,RAVNprocessed30milliondocumentsatarateofupto600,000perday(comparedwithateamoflawyersthatwouldhaveprocessed3,000perday).TheSFOisalsoconsideringotheradvancedMLtechniquesinotheraspectsofitsoperations.6

ARTOFTHEPOSSIBLEARTIFICIALINTELLIGENCE&BLOCKCHAIN|13

Processautomationandefficiency

RoboticProcessAutomation(RPA)17isgenerallyaprecursortotheintroductionofML.Humanerrorandalackofstandardisationinlegacyprocessesisaconsistentissuewhenmanagingriskandimprovingefficiency.

Alargeglobalbank18wantedtoautomateitsUKsalesquality(SQ)processtoimprove3keyareas:

compliancerisk-faultsanderrorsinuncheckedcases

goinguncorrected

accuracyrisk-inconsistencyandpotentialoversightsduetohumaninvolvement

slowprocesses-time-consumingreviewsanddelayedfeedbackloop

ROBOTICPROCESSAUTOMATION(RPA)

RPAisanapplicationoftechnology,governedbybusinesslogicandstructuredinputs,aimedatautomatingbusinessprocesses.UsingRPAtools,ateamcanconfiguresoftware,ora“robot,”tocaptureandinterpretdifferentapplicationsforprocessingatransaction,manipulatingdata,triggeringresponsesandcommunicatingwithotherdigitalsystems

TheSQteamisresponsibleforreviewingthesaleoffinancialproductsforregulatorycompliance.Currentlytheteamisrequiredtocheckasampleof10%to15%ofcompletedsales.Ateamof120humanreviewershadtolookatmorethan10differentdatasourcesand180datapointstofindandextract

theinformationtheyneededtocompleteanaudit.Eachreviewtookaround4hours.

ThebankworkedwithanAIserviceprovidertodevelopsoftwaretoautomatethisinternalcomplianceprocess.Thestructureddata,whichmadeup20%ofthetotaldata,wasfeddirectlyintothenewsystem.Theother80%wasunstructured.ThisincludedlettersandmemosinPDF,aswellaspayslipsandbankstatementssavedasimages.Atleast70%ofthechecksinvolvedintheSQprocessrequiredunstructureddata.

TheserviceproviderthendevelopedAImodelstoextracttherequireddata,usingadifferentmodelforeachdocumentcategory.Aftertesting,theAImodelscanextracttherequireddatafromunstructureddatasourcesmuchfasterandmoreaccuratelythanthepreviousmanualprocess.

UsingAIhelpedthebankbyproviding:

greatercompliance-theteamcanreview100%ofcases,insteadofjustasampleset

improvedaccuracy-automatedchecksusingAImodelsachievecloseto100%accuracy

fasterprocess-eliminationofthebacklogofchecksandmovingchecksclosertoreal-time

moretimeforteammemberstofocusonmakingimprovements

WhatisRPA?Arevolutioninbusinessprocessautomation,2018

HowaUK-basedbankusedAItoincreaseoperationalefficiency,2019

14|ARTOFTHEPOSSIBLEARTIFICIALINTELLIGENCE&BLOCKCHAIN

Risksofai

19ArtificialIntelligenceandPublicStandards,2020

Howdidyoudecidethat?

ThereareasexpectedsomedrawbackstoMLandDLmethods.Oneofthemostsignificantisthatifquestioned,theprocessbywhichAIsgothroughtoarriveattheirconclusionsisgenerallyunclearandknownastheblackboxcriticism.

Withneuralnetworks,forexamplewecanseetheinputsandtheoutputs,butthelayersofvariablesusedtogetfrominputtooutputarefairlyopaque.Insomescenariosthisisacriticalissueifwearetryingtopreventuncheckeddiscriminationorbiasinlendingmodelsforexample.

Unrecognisedbiascanbeasymptomofthemodelandoptimisationalgorithmnotbeingcalibratedcorrectlyorbeinggiventherightparameterstoscale,hencethecriticalimportanceoftestingandvalidationbeforerelease.

Askinghumanstotrustanoutputwheretheprocessitselfcannotbeexplainedorprovidedonrequesthasunquestionablydelayedadoptionofthetechnology.

However,asmoreAIsprovetheaccuracyoftheiranalysisandpredictions(withoutexplainingtheirprocess)wemay,afterenoughreliableoutputsfindourselvestrustingcertainAIsquickerthanexpected.

Ethics,responsibilityandstewardship

Aswehaveseenfromthe1980sexpertsystems,poorimplementationsofAIcanleadtodisastrousresults.TheCentreforDataEthicsandInnovationstatethat“Humansmustbeultimatelyresponsiblefordecisionsmadebyanysystem...Goodgovernancewillrequireforeachusecase,aspecificunderstandingoftheappropriatedivision

ofresponsibilities.”19

TheCommitteeonStandardsinPublicLifeinanimportant2020paperentitledArtificialIntelligenceandPublicStandards19hasidentifiedfiveareasofgovernancenecessaryforupholdingpublicstandardsinthiscontext:setting

responsibility;monitoringandevaluation;internalandexternaloversight;appealandredress;andtrainingandeducation.

WiththeamountofautomateddataanalysisandinterpretationinvolvedinAIimplementations,publicbodiesaboveallhaveanobligationtoensuretheyapplyandcanevidencegoodpracticeandthehigheststandardswhenembarkingonthisundeniablyvaluablebutunchartedjourney.

ARTOFTHEPOSSIBLEARTIFICIALINTELLIGENCE&BLOCKCHAIN|15

|

Whataboutmydata?

Anothersignificantriskparticularlyinthepublicsectorismanagingaccesstosensitivedatawhich,generallyspeaking,hastwokeyaspects:

providingtrustandconfidence

reducingtransactioncoststosustainablelevels.

Trustandconfidenceintheuseofsensitivedata

IntheNHSdataneedstobeprotected,forreasonsofprivacy,securityandconfidentiality.NHSdata-holdersneedtrustandassurancetobeconfidentinbeingabletosharedatawith

AIdevelopers.

TheInformationCommissioner’sOff