Auditing Algorithms : Towards Transparency in the Age of ...

AuditingAlgorithms:TowardsTransparencyintheAgeofBigData

ChristoWilsonAssistantProfessor@[email protected]

PersonalizationontheWebSantaBarbara,California Amherst,Massachusetts

PersonalizationisUbiquitousSearchResults

GoodsandServices

Music,Movies,Media

SocialMedia

DangersofPersonalization?

RacialDiscriminationChrisWilson

LookingforChrisWilson?Ad

FindPeopleNearYou!www.yellowpages.com

TrevonJones

TrevonJones,Arrested?Ad

SearchCriminalRecords,SexOffenderRegistry,andMore.

www.instantcheckmate.com

RacialbiasinGoogle’sAdSensesystemuncoveredbyLatanya Sweeneyin2013

Exampleofunintendedconsequences ofbigdataPeopleexhibitracialbiasintheirsearchandclickspatternsThead-placementalgorithmobservedandlearnedthesebehaviors

PriceDiscriminationShowingusersdifferentpricesInecon:differentialpricing

Example:Amazonin2001DVDsweresoldfor$3-4moretosomeusers

Surprisingly,notillegalintheUSAnti-DiscriminationActdoesnotprotectconsumers

Article20(2)oftheServicesDirectiveprotectsEUresidentsButcompaniesseemtobeflauntingtheregulation:(

WebsitesVaryPrices,DealsBasedonUsers’Information

PriceSteeringAlteringtheorderorcompositionofproductsE.g.highpriceditemsrankhigherforsomepeople

Example:Orbitz in2012UsersreceivedhotelsinadifferentorderwhensearchingNormalusers:cheaphotelsfirst;Macusers:expensivehotelsfirst

OnOrbitz,MacUsersSteeredtoPricierHotels

AuditingAlgorithmsGovernmentsandregulatorsareconcernedaboutbigdataandalgorithmsWhiteHousereports:BigData:SeizingOpportunities,PreservingValuesBigDataandDifferentialPricing

FTC’snewOfficeofTechnologyResearchandInvestigationTaskedwithmonitoringtheapplicationsofbigdataandalgorithms

Howdowemeasureandunderstandalgorithms?Algorithmsmaybetradesecrets,constantlychangingAccesstosourcecodeisnotenough,dataisequallyimportant

Emergingscientificarea:AuditingAlgorithms

GoalsofOurWork

1. UnderstandinghowcompaniescollectandsharedataaboutusersOnlineandofflineretailersAdvertisersandmarketersDatabrokerslikeAcxiom,Datalogix,Equifax,Experian,etc…

2. Reverse-engineeringonlinealgorithmstoassesstheirimpactSearchenginesOnlineadvertisementsE-commerceSocialnetworksetc…

MeasuringPersonalizationCaseStudy:GoogleSearchCaseStudy:E-commerce

MeasuringPersonalizationCaseStudy:E-commerce

AreAllDifferencesPersonalization?

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Compare

Notnecessarily! Itcouldbe:• Updatestoinventory/prices• Tax/Shippingdifferences• Distributedinfrastructure• Load-balancing

Howcanwereliablyidentifyandquantifypersonalization?

Personalization?

ControllingforNoise

129.10.115.14

129.10.115.15 74.125.225.67

Product 1Lorem ipsum dolor sit amet, consectetur adipiscing elit. In mollis


Queriesrunatthesametime

SameAmazonIPaddress

129.10.115.16


Noise

Difference – Noise = Personalization

IPaddressesinthesame/24

DualMethodology

REALUSERACCOUNTS

Leveragerealuseraccountswithlotsofhistory

Measurepersonalizationinreallife

SYNTHETICUSERACCOUNTS

Createaccountsthateachvarybyonefeature

Measuretheimpactofspecificfeatures

Questionswewanttoanswer:1. Towhatextentiscontentpersonalized?2. Whatuserfeaturesdrivepersonalization?

RealUserExperiment

TaskonAmazonMechanicalTurk(AMT)Over1000sofparticipantsEachexecutedhundredsofsearchqueriesEveryquerypairedwithtwocontrolqueriesRunfromemptyaccounts,i.e.nohistoryBaselineresultsforcomparison

HTTPProxy

UserQuery

UserQueryControlQuery

ControlQuery

ResultsfromRealUsers

0

5

10

15

20

25

30

35

40

45

50

1 2 3 4 5 6 7 8 9 10

ResultsChanged(%

)

SearchResultRank

Control/Control

RealUser/Control Differencebetweenresultsispersonalization

Topranksarelesspersonalized

Lowerranksaremorepersonalized

• Onaverage,realusershavea12%higherchanceofdifferingthanthecontrols• Mostchangesareduetolocation

WhatCausesofPersonalization?

HistoricalFeatures• LoggedIn/Out• HistoryofSearches• HistoryofSearchResultClicks• BrowsingHistory

AMTresultsrevealextensivepersonalizationNextquestion:whatuserfeaturesdrivethis?

StaticFeatures• Gender• Age• Browser• OperatingSystem• Location(IPAddress)• LoggedIn/Out

Methodology:usesynthetic(fake)accounts

LoggedIn/OuttoGoogle

0.5

0.6

0.7

0.8

0.9

1

1 2 3 4 5 6 7

Ave

rage

Jac

card

Inde

x

Day

No Cookies / No Cookies

Logged In / No Cookies

Logged Out / No Cookies

0

1

2

3

4

5

1 2 3 4 5 6 7A

vera

ge E

dit D

ista

nce

Day

Sameresults…Butina

differentorder

IPAddressGeolocation

0.5

0.6

0.7

0.8

0.9

1

1 2 3 4 5 6 7

Jacc

ard

Inde

x

Days

MA / MACA / MAUT / MAIL / MANC / MA

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1 2 3 4 5 6 7

Ave

rage

Edi

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tanc

eDay

Onaverage,1differentresult

…Plus1pairofreorderedresults

WhatAboutSearchHistory?Searchfor‘healthcare’ Searchfor‘obama,’ then‘healthcare’

Subsequentqueriesmay“carry-over”

ImpactofSearchHistory

00.10.20.30.40.50.60.70.80.91

0 2.5 5 7.5 10 12.5 15 17.5 20

AverageJaccardIndex

TimeBetweenQueries(Minutes)

OverlapinResults,Searchingfor‘healthcare’and‘obama’+‘healthcare’

10minutecutoff

MeasuringPersonalizationCaseStudy:E-commerce

TargetedRetailers10Generalretailers

BestBuyCDWHomeDepot JCPenney Macy’sNewEgg OfficeDepot SearsStaplesWalmart

Focusonproductsreturnedbysearches,20searchterms/site

6travelsites(hotels&carrental)CheapTickets Expedia Hotels.comPricelineOrbitz Travelocity

DoUsersSeetheSamePricesfortheSameProducts?

Manysitesshowinconsistencies forrealusersUpto3.6%ofallproducts

Retailers Hotels RentalCars

%ofP

roducts

InconsistentPrices

0

200

400

600

800

1000Differencein$

95th

75th

mean

25th

5th

HowMuchMoneyAreWeTalkingAbout?

Inconsistenciescanbe$100s!(perday/nightforhotels/cars)

Retailers Hotels RentalCars

WhatFeaturesTriggerPersonalization?Methodology:usesynthetic(fake)accountsGivethemdifferentfeatures,lookforpersonalizationEachdayfor1month,runstandardsetofsearches

Category Feature TestedFeatures

Account Cookie NoAccount,LoggedIn,NoCookies

User-AgentOS WinXP,Win7,OSX,Linux

BrowserChrome33,AndroidChrome34,IE8,Firefox25,Safari7,iOSSafari6

HistoryClick BigSpender,LowSpender

Purchase BigSpender,LowSpender

HomeDepotSmartphoneusersseetotallydifferent

productsthandesktopusers

7%ofproductshavedifferentpricesonAndroid

…butthepricesonlygoupby$0.50onaverage

TravelSitesCheaptickets andOrbitz offerlowerpricesonhotelsforuserswholog-intothesites1hotelperpage,$12offpernightonaverage

Travelocityoffersdiscountsonhotelsforusersonmobiledevices1hotelperpage,$15offpernightonaverage

Pricelinechangestheorderofsearchresultsbasedonclickandpurchasehistory

Exampleofpricesteering• 2accountsclick/reservehighpricehotels• 2accountsclick/reservelowpricehotels• 2accountsdonothing

Cheaptickets/Orbitz

Cheaptickets/OrbitzCheaptickets andOrbitz offerlowerpricesonhotelsforuserswholog-intothesites

About1hotelperpagehasalowerprice

Pricesdropbyaround$12pernight

Avg.PriceDifference($)

Travelocity

iOSusersseedifferenthotels

About1hotelperpagehasalowerprice

Pricedropsbyaround$15/night

Travelocityoffersdiscountsonhotelsforusersonmobiledevices

PricelinePricelinechangestheorderofsearchresultsbasedonclickandpurchasehistory

• 2accountsclick/reservehighpricehotels• 2accountsclick/reservelowpricehotels• 2accountsdonothing

Hotels.com/ExpediaHotelsandExpediaareconductinglarge-scaleA/BtestsontheirusersWhenyouvisitthesite,youarerandomly placedina“bucket”2outof3bucketsseehigh-pricehotelsatthetopofsearchresultsTheremainingbucketseeslow-pricehotelsatthetopofthepage

ExemplifiespricesteeringTheonlywaytoseethehiddenhotelresultsistoclearyourcookiesandreloadthesite

ConclusionsandFutureWork

TheEraofBigDataAlgorithmsdrivenbybigdatashapeyourworldSearchresultsyouaregivenPricesandproductsyouareshownMovie,music,andbookrecommendationsThedirectionsyouusetodrive

Inmanycases,thesesystemsarewonderful

Inothercases,theymaybedetrimentalUnintendedconsequencesIntentionalmanipulation

EligibilityforsocialservicesAccesstocreditandbankingAllocationofpoliceforces

OurGoal:TransparencyPersonalizationisproblematicwhenitisnottransparentHowisdatabeingcollectedandshared?Howisdatabeingusedtoaltercontent?

Usealgorithmauditstoinvestigatedeployedsystems,assesstheirimpact

OurgoalistoincreasetransparencyBuilding toolstohelpusersandregulatorsReverse-engineeringsystemstounderstandhowtheyworkRaisingpublicawarenessoftheseissues

PeekingBeneaththeHoodofUber

BordersonGoogleMaps

DiscriminationintheGig-economy

Allofourcode,data,andpapersareavailableat:

http://personalization.ccs.neu.edu

Auditing Algorithms : Towards Transparency in the Age of ...

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