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9/12/17–BuildingDataAcumen9/19/17–Incorpora4ngReal-WorldApplica4ons

9/26/17–FacultyTrainingandCurriculumDevelopment

10/3/17–Communica4onSkillsandTeamwork

10/10/17–Inter-DepartmentalCollabora4onandIns4tu4onalOrganiza4on

10/17/17–Ethics10/24/17–AssessmentandEvalua4onforDataSciencePrograms

11/7/17–Diversity,Inclusion,andIncreasingPar4cipa4on

11/14/17–Two-YearCollegesandIns4tu4onalPartnerships

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Provideinput,downloadtheinterimreport,andlearnmoreaboutthe

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Provideinputandlearnmoreaboutthestudyatwww.nas.edu/EnvisioningDSProvideinputandlearnmoreaboutthestudyatwww.nas.edu/EnvisioningDS

Ethics

SorinAdamMatei,PurdueUniversityProfessorandDirectorofPurdueData

StorytellingNetwork

BriHanyFiore-Gartland,UniversityofWashington

DirectorofDataScienceEthnographyeScienceInsBtute

3


Ethics


StorytellingNetworkandKredible.netProject

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Anethicalreasoning

frameworkfordatascienceeduca4on


Goalsofpresenta4on

•  Defineethicsandbigdata(therealmofdatascience)

•  Defineprinciplesforethicalreasoning•  Discusstheprinciplesinadatasciencecontext•  Presenttheethicalreasoningprocessincontext,usinganexample

•  Discusstheimplica4ons/answerques4ons

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Aprincipledapproachtoethics

•  Ethicsisamethodology.Dependsonmorality,whichisrootedinhumanvalues.– Dealswith“rightconduct.”

•  Necessitatesvalues:Whatvaluesshoulddirectethicalconduct?

•  Usedincontext:Whattypesoftrade-offsarepossibleandwhen?

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Ethicalconductinthecontextofdatascience

•  Whatarethemainethicalchallengesinvolvedindatascience?

•  Whatmoralprinciplesareaffectedbythesechallenges?

•  Howdocontextsofdatascienceuseorpoten4alabuseaggravateormi4gatetheurgencyofethicaldecisionmaking?

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DataScienceanditschallenges

•  Challengesderivedfromtheautoma4ccollec4onofbigdata–  Reuse

•  Genomicdatacollectedforonepurpose(diseasepreven4on)usedfordrugdiscovery

–  Recombina4on•  Joiningmul4pledatasetstoobtainfullerprofilesofusers(Google,Facebook)

–  Repurposing•  Datacollectedforbillingpurposesisusedforan4-terrorismsurveillance(theNSAmeta-datascandal)

–  Reanalysis•  Rawdatacanbeanonymous,analyzeddatacanbecomepersonallyiden4fiable(behaviorforensics:loginpa`erns,Wikipediaedi4ngpa`erns,etc.)

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Challenge1:Privacy

•  The4Rs(recombining,repurposing,reuse,reanalysis)implicitindatasciencemayaffectindividuallyprofoundlyanddirectly

•  Iden4ty,anonymitymaybecompromised->Privacymainconcern

•  Butisthisall?

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Privacyisnotsufficient

Concernforprivacyisaseriousissuebutdoesnotsuggestapathtoac4onStrategicques4on:whatdoeslossofprivacydotous?

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Lossofprivacyandcorehumanvalues

•  Privacylossisinfactpartofafamilyofmuchgeneralconcerns.– Limitsordenieshumanautonomy– Mayleadtotrea4ngpeopleunfairly– Breaksthebondsoftrustinsociety– Makessomepeopleworseofforexposesthemtorisk

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Corevaluesindatascienceethicalreasoning

•  Autonomy•  Jus4ce•  Trust•  Non-maleficence/beneficence

•  Howdowemakeprac4caluseofthem?

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Ethicalreasoningprocess

Doesthisprocedure,beitofanyform(collec4on,analysis,repurposing,recombina4on,etc.)leadto:•  Lossofautonomy?•  Lossoftrustbetweenindividualsorbetweenindividualsandtheresearcher?

•  Createunjustalloca4onofrewards(recogni4on,accesstoresources,socialadvancement)ordeleteriouseffects(s4gma,lossofface,materialloss,etc)?

•  Harmand/createsanetbenefitfortheusers?

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Sowhat?

•  Askingques4onsiseasy….

•  Answeringthemishard.

•  Howdowegoaboutansweringthem?

•  Inacontextualmanner….

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Contextualuseofvalues

•  Humanvaluesare(shouldbe)absolutes•  Valuejudgementsinthemselvesarenon-nego4able(jus4ce,trust,non-maleficencearepreferableunderallcircumstances.)

•  Whenconsideringtheeffectofvaluesonourac4onsweshouldestablishaminimumthresholdunderwhichweshouldandwecannotgo

•  Yet,forcertainsitua4onsandaboveacertainlevelofmoralcertainty,ethicalac4onsshouldbepragma4callydirectedbytrade-offanalysis

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Projectexample

•  Largescaleharves4ngofvideo-streamsfrompublicallyavailableweb-camsinna4onalparksbynon-governmentalactors(universityresearchers)

•  Declaredgoals:–  Reduc4onofhuman-wildlifeinterac4on–lesshumanandanimallimbandlifelossincidents

– Openupthedatatotheresearchcommunitytopredictincidentsandtargetpoten4alvic4msviasocialmediatoreducehuman-animalencounters

– Datacontainstensofthousandsofhoursoffootage,includingfaces,acts,andpersonallyiden4fiableinforma4onaboutparkvisitors

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Projectoutcomes

•  Findings– Reanalysis–revealswhoismorelikelytointeractwithanimals(gender,race,age)

– Recombiningwithsocialmediadata–iden4fiesrecentvisitorsataloca4onofgivengender,race,orage

– Repurposing–iden4fiedvisitorsaretargetedwithcustomizedmessagesviaTwi`ertoavoidfutureencounters

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Ethicalreasoningprocess

•  Yes,if:–  Thegoalisnon-maleficentorevenbeneficent

•  Inthiscasetolimitlossofhumanandanimallifeorlimb–  Autonomyisnotlimited

•  Inthiscase,individualsarenotdeniedaccesstothepark,theyaresimplyinformed–  Jus4ce

•  Acasecanbemadethatsomeindividualsmaybetargetedbyrace,gender,ageaffilia4onregardlessiftheydidnotinteractwithanimalsinanunsafeway

–  Trade-offanalysis–isthesocietalbenefitgreaterthanthemildannoyanceoftheindividualsofreceivinginforma4vetweets?weresomefundamentalrightsdeniedtotheseindividualsorsomeprivilegesbestowedtosomeandnottothem?

–  Trust•  Doesthiscampaigninspireorerodetrust?

–  Tradeoffanalysis–whilesomemightfindthecampaignintrusive,ifrightlypreparedbypos4ngnotesnexttocamerasthatinformabouttheiruse,itmayleadtoanincreaseoftrustinthosethataretryingtolimithumanoranimallifeandlimbloss

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Whatdidwejustdo?

•  Weengagedinethicalreasoning•  Valuebased,withaminimumthresholdofvalueconcernandcontext-basedtrade-offanalysis

•  TheprocessisdescribedinEthicalReasoninginBigData,co-editedwithJeffCollmann

•  TheethicalprocessdescribedwasshapedinamajorwaybyMichaelSteinmannfromStevensIns4tuteofTechnology

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h`p://amzn.to/2kOwTDz


Ques4ons?

•  Moreinforma4on– h`p://kredible.net– h`p://matei.org/ithink

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Ethics



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EthicalThinkingforDataScience

Educa4on


Datajus4ceandresponsibility

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•  Democra4za4onofdatasciencetoolsandtechniques

•  Computa4onalresearchers

workinginunfamiliarfieldsorcontexts

•  Secondaryusesofdata;Datacollectedforonepurposeandusedforanother

•  Shincultureofethicalcompliancetocultureofethicalthinking

Provideinputandlearnmoreaboutthestudyatwww.nas.edu/EnvisioningDS 23

Course developed by Jevin West and Carl Bergstrom, University of Washington


DataPipeline

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Human-DataPipeline

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Inspired by Dr. Seuss


Spurious correla-ons (Tyler Vigen)


BigDataTruthiness Implicit assumption and expectation that big

data has the quality of seeming or being felt to be true, even if not necessarily true.

(Adapted from Stephen Colbert’s “Truthiness”)


DataSciencechallenges

currentethicalframeworks

Theperils!

CaseStudy:Uber“RidesofGlory”

analysis


Uber’s“RidesofGlory”Analysis

•  Ridesfrom10pm-4amonFri/Sat,followedbyridefromsameloca4on4-6hrslater

•  Impliespeople“foundlovethatyoumightimmediatelyregretuponwakingupthemorninganer”

•  Callsthese“RidesofGlory”


Uber’s“RidesofGlory”Analysis

• Consent• Privacy• Interpreta4on


DataScienceasEthical

Interven4onThepromise!

CaseStudy:Amazonsame-daydeliveryservice:Canalgorithmsberacist?


Thankyou!

35

Contact:[email protected]

Resources:

•  CallingBullshitsyllabusandreadings,tools,editedvideosoflecturesandcasestudiesareallavailableatwww.callingbullshit.org

•  [email protected]•  Linktodatascienceethicscasestudiesandfurtherreading,

h`ps://4nyurl.com/y9meel3s


Ethics–Q&A


StorytellingNetwork



36

9/12/17–BuildingDataAcumen9/19/17–Incorpora4ngReal-WorldApplica4ons

9/26/17–FacultyTrainingandCurriculumDevelopment

10/3/17–Communica4onSkillsandTeamwork

10/10/17–Inter-DepartmentalCollabora4onandIns4tu4onalOrganiza4on

10/17/17–Ethics10/24/17–AssessmentandEvalua4onforDataSciencePrograms

11/7/17–Diversity,Inclusion,andIncreasingPar4cipa4on

11/14/17–Two-YearCollegesandIns4tu4onalPartnerships

(recordingposted)

(recordingposted)

(recordingposted)

Provideinput,downloadtheinterimreport,andlearnmoreaboutthe

studyatwww.nas.edu/EnvisioningDS

(recordingposted)

(recordingposted)

provide input and learn more about the study at ...

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