人工智能在灾害风险管理中的公平性和问责性：机遇和挑战 Fairness and accountability of AI in disaster risk management Opportunities and challenges

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Perspective

FairnessandaccountabilityofAIindisaster

riskmanagement:Opportunitiesandchallenges

CarolineM.Gevaert,

1

,

*

MaryCarman,

2

BenjaminRosman,

3

YolaGeorgiadou,

4

andRobertSoden

5

1DepartmentofEarthObservationScience,FacultyITC,UniversityofTwente,Enschede,Overijssel7514AE,theNetherlands

2DepartmentofPhilosophy,FacultyofHumanities,UniversityoftheWitwatersrand,Johannesburg,Gauteng2000,SouthAfrica

3SchoolofComputerScienceandAppliedMathematics,FacultyofScience,UniversityoftheWitwatersrand,Johannesburg,Gauteng2000,SouthAfrica

4DepartmentofUrbanandRegionalPlanningandGeo-InformationManagement,FacultyITC,UniversityofTwente,Enschede,Overijssel7514AE,theNetherlands

5DepartmentofComputerScienceandSchooloftheEnvironment,UniversityofToronto,Toronto,ONM5T1P5,Canada*Correspondence:

c.m.gevaert@utwente.nl

/10.1016/j.patter.2021.100363

THEBIGGERPICTUREArtificialIntelligence(AI)isincreasinglybeingusedindisasterriskmanagementap-plicationstopredicttheeffectofupcomingdisasters,planformitigationstrategies,anddeterminewhoneedshowmuchaidafteradisasterstrikes.ThemediaisfilledwithunintendedethicalconcernsofAIalgo-rithms,suchasimagerecognitionalgorithmsnotrecognizingpersonsofcolororracistalgorithmicpredic-tionsofwhetheroffenderswillrecidivate.WeknowsuchunintendedethicalconsequencesmustplayaroleinDRMaswell,yetthereissurprisinglylittleresearchonexactlywhattheunintendedconsequencesareandwhatwecandotomitigatethem.Theaimofthisperspectiveistocallresearchersworkingonfair-ness,accountability,andtransparencytoworkwithDRMandlocalexperts—sowecanensurethatdisastermitigationandreliefisaccountable,considerslocalvalues,andisnotunintentionallybiased.

Concept:Basicprinciplesofanew

datascienceoutputobservedandreported

SUMMARY

Disasterriskmanagement(DRM)seekstohelpsocietiespreparefor,mitigate,orrecoverfromtheadverseimpactsofdisastersandclimatechange.CoretoDRMaredisasterriskmodelsthatrelyheavilyongeospatialdataaboutthenaturalandbuiltenvironments.Developersareincreasinglyturningtoartificialintelligence(AI)toimprovethequalityofthesemodels.Yet,thereisstilllittleunderstandingofhowtheextentofhiddengeo-spatialbiasesaffectsdisasterriskmodelsandhowaccountabilityrelationshipsareaffectedbytheseemergingactorsandmethods.Inmanycases,thereisalsoadisconnectbetweenthealgorithmdesignersandthecommunitieswheretheresearchisconductedoralgorithmsareimplemented.Thisperspectivehigh-lightsemergingconcernsabouttheuseofAIinDRM.Wediscusspotentialconcernsandillustratewhatmustbeconsideredfromadatascience,ethical,andsocialperspectivetoensuretheresponsibleusageofAIinthisfield.

INTRODUCTION

Climatechangeandpopulationgrowthinurbanareasareincreasingtheriskofpersonsandinfrastructuretodisasters.In2021alone,disastersaffectedmorethan98.4millionpeople,

includingmorethan15,000deathsandanestimatedeconomiclossofUSD171.3billion.

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Disasterriskmanagement(DRM)

aimstohelpsocietiesrecoverfromdisastersandprepareforandmitigatetheimpactoffuturedisasters.GeospatialdataplayakeyroleinDRMbymappingpopulationsandinfrastruc-tureexposedtodisasters,identifyingareasatrisk,andplanning

emergencyresponses.

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Remotelysensedimageryfromsatel-litesordronescanextractproxiestoanalyzepre-disastervulner-abilityandresilienceandpost-disasterdamageandrecovery.

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Aswithotherdomains,DRMhasrecognizedthepotentialofarti-ficialintelligence(AI)algorithmstorapidlyandaccuratelypro-cessdata,andisnowusingAItodevelopmoreaccurateriskmodelsandprioritizethedistributionofdisasteraid.

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DespitethegreatpotentialofAItosupportDRMprocesses,practitionersexpressconcernsregardingtheethicalandresponsibleusageofthesealgorithms.Howcanapractitionerbecertainthattheirriskmodelisnotbiasedagainstthemost

Patterns2,November12,2021ª2021TheAuthors.1ThisisanopenaccessarticleundertheCCBYlicense(

/licenses/by/4.0/

).

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vulnerablesocietalgroupsinacity?Howareaccountabilityrela-tionshipsindisasteraidinfluencedbytheintroductionofAIwhenalgorithmdevelopersarefarfromthedisasterandunfamiliarwiththelocalcontext?Aplethoraofguidelinesonethicalorrespon-

sibleusageofAIisemerging,eachpromotingslightlydifferentvaluesanddefinitions.See,forexample,theextensivecompar-isonofleadingguidelinesfor‘‘principled’’AIbyFjeldetal.

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TheethicalorresponsibleusageofAIoftencirclesaroundahandfulofimportantconcepts,includingfairness,accountability,andtransparency(FAccT).Fairnessornon-discriminationgenerallyrefertotheabsenceofbiasindatasetsandalgorithms.Researchshowsthatalgorithmstrainedonbiaseddatasetsfailtorecog-nizehistoricallyexcludedgroupsinsociety(e.g.,BarocasandSelbst

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).Accountabilityrelatestothemechanismorprocessthroughwhichaforumcanholdanactortojustifytheiractions(seeOlsonetal.,

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pp.60–61).InanAIcontextaccountability

thenturnstodiscussionsonwhoistheactor(thedeveloperofthealgorithm?theorganizationdeployingthealgorithm?),andhowtheiractionscanbejustified.Thelatterrequirestranspar-encyorexplicability,andmuchresearchisfocusedonexactlyhowwecanshedlightontheinnerworkingsofcomplexAIalgo-rithmstoenablethisjustification.SuchconcernsregardingFAccTarebeingthoroughlyinvestigatedbysocialanddatasci-

entistsforapplicationsofAIindomainssuchascriminaljusticeandeducation,

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butnotyetinthefieldofDRMandgeospatialsciences.

Theobjectiveofthisperspectiveistocallresearchersinter-estedinFAccTandotherethicalconsiderationsofAItolooktowardthefieldofDRM.TheperspectivefirstprovidesabriefintroductiontoFAccTresearchandthendelineatespotentialconcernsobservedbyDRMexpertstoemphasizethedemandforFAccTresearchandsolutionsintheDRMcommunity.Wethencontinuetodescribethetechnicalaspectsofgeospatialdata,whichisprominentinDRMapplications,andhowitspecu-liaritiesrequiretinkeringwiththetechnicaltoolstoauditdataandpreserveFAccTprinciplesinDRMworkflows.Thirdly,weemphasizehowDRMapplicationshighlighttheneedforinclusiv-ity,includingvulnerablepopulationsinholdingdistant,bigdataalgorithms,andinternationalcorporationstoaccount,andinclu-sivityinthewaythatFAccTresearchisconductedandvaluesaredefined.Finally,weproviderecommendationsonhowtostartintegratingthesetwofieldsmoreclosely.

FAIRNESS,ACCOUNTABILITY,ANDTRANSPARENCY

TheriseofAIhasgalvanizedtheintegrationofautomatedalgo-rithmsintodecision-makingsystems.However,thereisalsoanincreasingconcernregardingtheethicalimplicationsofthesesystems.ResearchintotheethicalaspectsofAIsystemsisknownasethicalAI,FAT,FAT/ML,FAT*,orFAccT.FATreferstothethreeconcepts:FAccT.TheadditionofMLreferstoappli-cationsrelatedtomachinelearning,and*isatypeofwildcardemphasizingthatotherethicalcomponents,suchasinclusivity,power,andjusticearealsoconsideredinthisfieldofresearch.FAccTisthelatestdenominationadoptedbyoneofthemost

influentialconferencesinthefield:theACMConferenceon

Fairness,Accountability,andTransparency(ACMFAccT).

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ResearchinthefieldofFAccTisslightlyparadoxicalasitaims

todeveloptechnicalsolutionstoauditandensureethicalvalues

2Patterns2,November12,2021

Perspective

inAIworkflows,althoughthevaluesthemselvesareambiguousbynature.Forexample,fairnessgenerallyreferstoalackofbiasinanAIsystemagainstacertainindividualorgroup,buttherearemultipledefinitionsoffairnessutilizedintheFAccTliterature.Someadoptastatisticalapproachbasedonsimilarperformanceinclassificationmetrics,e.g.,thatdifferentculturalgroupsshouldhavethesamechancesofachievingadefinedalgo-rithmicoutput.Otherapproachestestwhetherapredefined

sensitivevariable(e.g.,gender)influencestheoutputofanalgo-rithm.

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Tocomplicatemattersevenfurther,althoughmanydef-initionsoffairnessachievesimilarresults,theyaresensitivetodatavariabilityandsomedefinitionsmayachieveconflictingout-puts.

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Indeed,Kleinbergetal.

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gosofarastoprovetheoreti-callythatsomecommonlyaccepteddefinitionsoffairnessareincompatible.ItisnotthepurposeofthisperspectivetogiveanextensivecomparisonofdifferentfairnessmetricsorotherethicalvaluesresearchedintheFAccTfield.Forthis,thecuriousreaderisencouragedtoconsultexcellentreviewsonfairness(e.g.,VermaandRubin

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),accountability(e.g.,Wieringa

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),andtransparency(e.g.,Mittelstadtetal.

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)intheliterature.However,forthecontextofthisperspectiveitisimportanttorecognizewhyresearchinthefieldofFAccTrequiressuchclosecollaborationbetweendatascienceandhumanities,aswellasknowledgeofthecontextinwhichthealgorithmwilloperate.

TheFAccTcommunityismadeupofresearchersfromma-chinelearning,statistics,datascience,law,andsocialsciences,aswellasinterestedindustrybodies,suchasGoogle,IBM,andMicrosoft,asillustratedbytheparticipantsintheannualACMFAccTconference.

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Nevertheless,asignificantcritiqueofFAccTresearchingeneralisthat,evenifperfecttechnicalsolutionsforFAccTvaluescouldbeembeddedintoAIsystems,thereisgenerallyalackofunderstandingregardingthesocial,cultural,andpoliticalenvironmentinwhichthesesystemsarede-ployed.

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,

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Indeed,FAccTconferencesremaindominatedbyAmericaninstitutesandmainlywhitemaleauthors,

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althoughtherearestrongeffortstoincreasediversity.Theseconcerns

highlightthetimelinessofourcallforFAccTresearcherstoconsiderinvestigationsintheDRMdomain.Thismanuscriptem-phasizesrichopportunitiesprovidedbyDRMtodevelopthetheoreticalframeworkofFAccT,newtechnicalsolutions,andboostinclusivity.

POTENTIALCONCERNSARTICULATEDBYTHEDRMCOMMUNITY

SeveralbroadcategoriesofconcernarisewhenintroducingAIintoDRM.DrawingonrecentworkintheDRMcommunity,

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wecanhighlightthreeofthemhere.

Firstly,biasisarecurringtopicofdiscussionamongrespon-sibleAIpractitioners.GiventheglobalscopeofDRMactivities,andwidevariationsinqualityandcoverageofgeospatialdata,biasisindeedasignificantrisk.Forexample,calldetailrecordsgeneratedbymobilephonesmaybeusedtoestimatepopulationsizesbeforeandafteradisaster,

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butmayunderestimatevulnerablepopulationswhohavenoaccesstocellphones.

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Oftenconceivedofasstatisticalerrordrivenbychoicesaroundsamplingdatabydatascientistsandtechnicalexperts,biasintheresponsibleAIliteratureisoftenviewedmorebroadly.

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Thus,biascanalsobetheresultofalgorithmdesignordecisions

Perspective

aroundmetricsusedtoevaluateaparticularphenomenon.Whilesometechnicalapproachestoaddressingbiasexist,completelyeliminatingbiasinalgorithmsisimpossible,and,assomehaveargued,

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,

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exclusivefocusonreducingbiasinAIsystems

maydistractfromother,moreimportant,interventions.Asecondconcernrelatestotransparencyandaccountability.

TheintroductionofAItechniquesandtheirassociated

complexityintodisasterriskmodelingprocessesmayreducetheabilityofgovernment,thepublic,andotherimportantstake-holderstomeaningfullyparticipateinDRM.Evenmodelersthemselveshavereportedthat,insomecircumstances,theirabilitytounderstandandevaluatetheoutputsofAImodelshasdecreasedincomparisonwithtraditionalapproachestodisasterriskmodeling.

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,

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Indeed,thefieldofexplainableAIwascreatedlargelytoaddressthisissue.Forexample,Behletal.

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usedexplainableAItoinvestigatethepotentiallimitationsofanalgorithmtrainedtoprocessTwitterdatatoidentifypeo-ple’sneedsafteradisaster.However,ingeneralthe‘‘explana-tions’’providedbyexplainableAIaredissonantfromhowhumanbeingstypicallyconstructexplanations.

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Withreducedtrans-parencyofcomplexAImodelscomesincreasedchallengesinensuringthatexpertsanddecision-makersinvolvedinDRMareaccountabletothecommunitiesthattheyserve.

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Finally,thehypeandinflatedexpectationsthatsurroundAIatthemomentmayleadtooneormoreinterrelatedproblems.Mostdirectly,untestedorimmatureAItoolsmaybeusedinsafety-criticalsituationsforwhichtheyarenotyetready.Thismayinturndrawneededresourcesandattentionawayfrommoresuitableapproachesorencourageover-relianceontoolsthatarenotfitforpurpose.Disastersareoftenviewedasoppor-

tunitiesforinnovation,butinthehandsofuncarefulorunscrupu-

lousdevelopersthiscanbearecipeforharm.

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Agoodexampleofavoidinghypeisgivenbyforecast-basedfinancing,intro-ducedbytheRedCrossRedCrescentmovementtoreleasedisasterresponsefundingbeforetheimpactofadisaster.Thereleaseoffundsiscontingentonthepredictedimpactofapendingdisaster,whichcanbemodeledbyAI.However,avali-dationcommitteenotonlychecksthevalidityoftheproposedAI

model,butalsowhethersimpler,expert-basedsystemswouldbemoreappropriate.

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ADAPTATIONSOFTECHNICALSOLUTIONSTO

GEOSPATIALDATA

Takingbiasasanexample,wecanshowhowDRMapplicationscanbenefitfromthetechnicalsolutionsdevelopedbyFAccTre-searchersandhowDRMapplicationsprovideopportunitiesforFAccTresearcherstodevelopnewsolutions.DRMapplicationsoftendependongeospatialdata.Satelliteanddroneimageryprovidesnapshotsoftheworldbelowandcanbeusedtogeneratemapsofbuildingsandimportantinfrastructure.Mobilityandsocialmediadatacanprovideinsightsintothemovementofcitizens.Hazardmodels,suchasfloodmodels,showthespatialextentoftheareaatrisk.Unfortunately,boththeDRMandFAccTcommunitiespaylittleattentiontobiasesthatareembeddedingeospatialdata.

Thereisawell-knownlackofup-to-date(geospatial)datainlow-tomiddle-incomecountries(LMICs)comparedwithhigh-in-comecountries(HICs)(e.g.,theCenterforHumanitarianData

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OpenStreetMap(OSM)inconjunctionwithhumanitarianmap-pingeffortshaveaimedtoimprovethedisparityindataavailabil-ityofHICsversusLMICs,andyetasignificantgapremainsashumanitarianmappingeffortsseemtofocusonareasofpastdi-sasters,areascontaininglocalmappingcommunities,andareas

ofinterestforspecificstakeholders,suchasdevelopmentagencies.

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Littleinformationisavailableforruralorunprioritizedareas.Similarly,mobilityandsocialmediadataexcludesthose

withoutadigitalfootprint,suchasdisadvantagedcommunities

withlimitedaccesstodigitaltechnology.

ManyAIalgorithmsareusedtoprocessthiskindofgeospatialdata.Backinthe1950s,methodsconsistedofspatialinterpola-tionthroughkriging(morerecently,Gaussianprocesses)orsim-pledecisiontrees.Expert-basedmachine-learningsystemsbecamepopularinthe1980sandthisshiftedtodata-drivenma-chine-learningtechniquessuchassupportvectormachinesinthe1990sandrandomforestsattheturnofthemillennium.

Thelasttenyearshavebeenheavilyinfluencedbydevelopmentsincomputervision,anddeeplearningtechniquesarenowbeingwidelyappliedtogeospatialdata.

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Thesetechniquesandalgo-rithmsareleadingtounprecedentedclassificationaccuracies

andshowmuchpromiseforDRMapplications.However,theyalsosufferthesamevulnerabilitiesidentifiedinotherdomainsusingthesealgorithms,suchassusceptibilitytobias.

FAccTresearchersaredevelopingtechnicaltoolstoidentifyandmitigatebiasinsuchAIalgorithms.SomeofthesesolutionscandirectlybeappliedtoDRMapplications.Forexample,SureshandGuttag

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illustratetheroleofhistoricalbias,repre-sentationbias,measurementbias,aggregationbias,evaluationbias,anddeploymentbiasinmachine-learningalgorithms.ThesesamebiasescaneasilybeidentifiedinDRMworkflows.Historicaldatausedtotrainhazardmodelsmaynottaketheim-pactsofclimatechangeintoaccount

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(i.e.,historicalbias)andconflictingdefinitionsusedtoidentifyvulnerablepopulationsmaygrosslyunderestimatethepopulationlivinginpoverty

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(i.e.,measurementbias).

Inothercases,adaptationsareneededwhenapplyingdevel-opedtechniquestogeospatialdataandDRMapplications.Auditingforbiasesoftendependsontheidentificationofsensi-tiveattributes.Well-knownexamplesofrepresentationbiasandevaluationbiasincludetheunderrepresentationofracesorgenderintrainingandevaluationdata.However,itisnotclearwhichtypesofgroupsmaybeunderrepresentedingeospatialdataandthushowthesedatashouldbeauditedtocheckforpo-tentialbiases.

SometimessuchsensitiveattributescanbedirectlyidentifiedinDRMapplications.Forexample,householdsurveysorotherdemographicdatathatcontainsensitivedatamaybeutilized.

Theuseofsocialmediafordisasterwarningsandpost-disaster

damageassessmentmaycontainbiasesongender,income

level,andminoritygroups.

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Inothercases,thegeospatialdatausedforDRMdoesnotdirectlyspecifysensitiveattributesbutcanindirectlycapturein-formationthatisrelatedtosocio-economicorculturalgroups.Forexample,thebodyofresearchoninformalsettlementmap-pingoftenrelatesthephysicalcharacteristicsofbuildingstothesocio-economicstatusofitsinhabitants.Characteristicsidentifi-ableinremotelysensedimagery,suchassmallbuildings,low-qualityroofingmaterials,irregularstreetpatterns,andnarrow

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Data/Actors

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Perspective

Newhumanitarianism/DRM

Digitalhumanitarianism

Figure1.Anoverviewoftheevolutionofhumanitarianactioninfourstages

RemotePublicaccountability

Aim:Legibility

Algorithmicaccountability

Aim:Prediction/preemption

(2)(3)tionalCommitteeoftheRedCrossincon-

flictsituations.DRMbecamepractically

synonymouswithnewhumanitarianismin

ClassicalhumanitarianismNeoclassicalhumanitarianismtheearly2000swiththedistinctionbe-

ThickaccountabilityHybridaccountabilitytweenthetwofieldsbecomingincreas-

Aim:CareofthewholepersonAim:Epistemicjusticeinglyblurredinthedigitalandalgorithmic

era,especiallyafter2010.Themergingof

Proximate(1)(4)thetwofieldswasaccompaniedbycalls

foraccountability,itselfavaluethathas

ExpertsystemsMachinelearninghaddifferentmeaningsovertime.Tracing

(Spatial)technologythehistoricalevolutionofhumanitarian

action,especiallyafteritmergedwithDRM,aswellastherelatedmeaningsof

accountabilitymayhelpusidentifyand

streetsarestronglyrelatedtoimpoverishedlivingconditions.

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salvagevaluableconceptsfromclassicalhumanitarianism—a

Mapsproducedthroughmachinelearningcouldthereforebesymbolofglobalmoralprogressandahumanizerofthe

auditedregardingwhichtypesofobjects(suchasbuildingtypes)world

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—intheeraofmachinelearning.

maybeunderrepresentedinadditiontothegeographicaldistri-

Figure1

capturesfourstagesintheevolutionofhumanitarian

butionofthetrainingdata.Closecommunicationwithstake-action.Thehorizontalaxisrangesfromexpertsystemsrepli-

holdersandtheDRMapplicationwoulddefinewhichphysicalcatinghumandecisionrulestomachinelearningthatgenerates

characteristicsorobjectsshouldbeconsideredaspossiblypredictivemodelsusingtechniquessuchasprobabilistic

sensitive.reasoning.Weusetheterm‘‘expert’’broadlytorefertoanexpert

Anotherimportantfactorrelatestothedistanceoftheremotenotasaspecialkindofpersonbuttoeverypersonasaspecial

mapperfromthelocalcontextandthepowerbiasindecidingkindofexpert,especiallywithrespecttotheirownproblems.

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whatistobemapped.ResearchbyLMICsisgrosslyunderrep-Theverticalaxisrangesfromactorsinface-to-faceproximity

resentedintheDRMresearchcommunity.

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Similarly,thewithbeneficiariestoactorsveryremotefrombeneficiaries—for

perceptionofwhichtypeofinformationisimportantforaddress-example,corporatephilanthropists,commercialgeospatialand

ingDRMissuesisoftendefinedinHICsandmayoverlookimpor-mobilephonecompanies,self-organizingvoluntarynetworks

tantlocalnormsandcontexts,

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whicharecrucialforsolvingofdigitalhumanitarians,universities,andinternationalspace

complexsocialissues.Visualanalysesofdroneimageryinagencies.Thesameverticalaxisrangesfromdatacollectedin

slumareasbylocalresidentsillustratesthattheperceptionsofface-to-faceinteractionsinthefieldtodatacollectedbyremote

sensitiveobjectsvariedgreatlyindifferentareas

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andempha-digitalhumanitarians,satellites,drones,mobilephonecom-

sizesthatinterpretingremotesensingimageryiscontextdepen-panies,andthelike.Thetwoaxesspanaspacethatallowsus

dent.Thelackofunderstandingoflocalcontextmayexcludetoconceptualizetheevolutionofhumanitarianactionovertime.

localassetsandvaluesfrombeingrepresentedingeospatialInclassicalhumanitarianism(cell1)theaimisthe‘‘careofthe

datautilizedforDRMandthusinhibitstheabilityofthesedatawholehumanperson,allofherorhim.’’

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Thethicklyaccount-

tosupportthedevelopmentoflocallyeffectivemitigationablehumanitarianhasempathyandcompassionforvictimsof

measures.crises,makesherselfvulnerableandreachableinthefield,

TheexampleofbiasthusillustratesbothhowDRMapplica-mustearntherespectofvictimsandunderstandothers’

tionscanbenefitfromthetechnicalsolutionsdevelopedbysuffering.Newhumanitarianism(cell2)foregroundspublic

FAccTresearchersandhowDRMapplicationsprovideopportu-accountability—therightofempoweredcitizenstoholdstateac-

nitiesforFAccTresearcherstodevelopnewsolutions.Weturntorstoaccountforfailurestomaketheterritoryandthepeople

nowtoconsideringhowquestionsofaccountabilityandinclusiv-legibleandgovernancerulesenforceable,forwhendisaster

itytieinwithDRM,firstthroughthelensofhumanitarianismandstrikes.DRMscholarsdefinepublicaccountabilityas:

Arelationshipbetweenanactorandaforum,inwhich(a)

theactorhasanobligationtoexplainandjustifyhisorher

plansofactionand/orconduct,(b)theforummaypose

questions,requiremoreinformation,solicitotherviews,

andpassjudgement,and(c)theactormayseepositive

ornegativeformaland/orinformalconsequencesasa

result(Olsonetal.,

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p.61).

thenbytakingastepbacktoconsidertheverywayinwhich

valuesaredefined.Again,weaimtohighlighttheinterplay

betweenFAccTconsiderationsandDRM.

ACCOUNTABILITYINNEOCLASSICAL

HUMANITARIANISM:EPISTEMICJUSTICE

DRMasafieldofresearchandpracticeistheprogenyofclas-

sicalhumanitarianism,whichstandsforthelife-savingreliefThe2010Haitiearthquakeisacaseinpoint.AsOlsonetal.

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assistanceandprotectionhistoricallyprovidedbytheInterna-argue,t