auditing algorithms : towards transparency in the age of
TRANSCRIPT
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
Product 2Lorem ipsum dolor sit amet, consectetur adipiscing elit. In mollis
Queriesrunatthesametime
SameAmazonIPaddress
129.10.115.16
Product 2Lorem ipsum dolor sit amet, consectetur adipiscing elit. In mollis
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
MeasuringPersonalizationCaseStudy:GoogleSearchCaseStudy:E-commerce
ResultsfromRealUsers
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ResultsChanged(%
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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
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No Cookies / No Cookies
Logged In / No Cookies
Logged Out / No Cookies
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Sameresults…Butina
differentorder
IPAddressGeolocation
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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:GoogleSearchCaseStudy:E-commerce
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
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1000Differencein$
95th
75th
mean
25th
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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