CS388: Natural Language Processing Lecture 26: Wrapup + Ethics
Transcript of CS388: Natural Language Processing Lecture 26: Wrapup + Ethics
Administrivia
‣ FinalprojectsdueDecember14
‣ ProjectpresentaHonsnextTuesdayandThursday
‣ PleasefilloutthecourseevaluaHonifyouhaven’talready!Timeattheendofclasstoday
Unsup:topicmodels,grammarinducHon
Collinsvs.Charniakparsers
Abriefhistoryof(modern)NLP
1980 1990 2000 2010 2017
earlieststatMTworkatIBM
“AIwinter”rule-based,expertsystems
Penntreebank
NP VPS
Ratnaparkhitagger
NNP VBZ
Sup:SVMs,CRFs,NER,SenHment
Semi-sup,structuredpredicHon
Neural
‣Whatdifferentmodelstructuresdidweconsider?
SequenHalStructure:Analysis‣ LanguageisinherentlysequenHal
BarackObamawilltraveltoHangzhoutodayfortheG20mee=ng.
PERSON LOC ORG
B-PER I-PER O O O B-LOC B-ORGO O O O O
y1 y2 yn…
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‣ Candolanguageanalysiswithsequencemodels
SequenHalStructure:GeneraHon
themoviewasgreat
le
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film était bon [STOP]
OnFriday,theU.S.intendstoannounce…
…
U.S.tolimsancHonsFriday
‣ TranslaHon:
‣ SummarizaHon:
TreeStructure:Analysis
DT NNTOVBDDT NNthe housetoranthe dog
ROOT
‣ ParsetreesexposeandlocalizetherightinformaHonmoredirectly:
‣ SemanHcroles:(ran,SUBJ=dog,IOBJ=house)
‣ AMRsthatincludecoreference,etc.
TreeStructure:Analysis‣ UsefulincombinaHonwithneuralnetworksfortaskslikesenHmentanalysis
themoviewasgreat
‣ Howmuchdoexplicittreeshelp?
TreeStructure:GeneraHon
Rabinovichetal.(2017)
‣ Generatestructuredthingslikesourcecode
‣ Generatesentences?Maybe…
TreeStructure:GeneraHon
slidecredit:DanKlein‣ CurrentbestMTsystemsarearguablyword(orevencharacter!)level,butalsoarguablymoreabstract…
Higher-levelStructure:Documents‣ LatentmodelsofdiscoursestructurecanhelpwithsenHmentanalysis
LiuandLapata(2017)
‣ SummarizaHon:explicitmodelsofdiscoursearen’tallthatuseful,butimplicitonesmightbe
Higher-levelStructure:IE/QA‣ CombineinformaHontomakededucHonsandreasonacrosssentences
Kacely is a 12-year-old girl. She currently goes to Sunshine Middle School .
Q: Kacely is a ____?
She'salovelygirl.Shehaslongandblackhair.Sheisquitetallandslim.Hereyesarebrightandblack.Sheis13yearsold.Sheisgoodatsinging.Shelikeslisteningtomusic.SheisS.H.E.'sfan.DoyouknowConan?HeisalivledetecHve.Thelovelygirlalsolikeshim.Oh,sorry.Iforgettotellyouwhothegirlis.It'sme.I'malovelygirl.YoucancallmeKacelyorKacelin.NowIstudyatSunshineMiddleSchool.I'minClass1,Grade7.Everyday,Igetupat6:00a.m.Theclassesbeginat7o'clock.IlikelunchHmebecauseIcanchatwithmyfriendsatthatHme.Amerschool,Iusuallyplaybadmintonwithmyfriends.IlikeplayingbadmintonandIamgoodatit.IwanttobeasuperstarwhenIgrowup.
A) student B) teacher C) principal D) parent
She Kacely
Kacely goes to school
Kacely goes to school ENTAILS Kacely is a student
coreference
entailment
parsing
Higher-levelStructure
Raisonetal.(2018)
‣ There’salimittohowfarwecandesigndeepneuralnetworksthatworkwell
‣ Simplearchitectures+lotsofdata(BERT)
‣ InducHvebias,logicalreasoning?
Wheredowegofromhere?‣ Neuralnetworksletuslearnfromdatainanend-to-endway,verypowerfullearners
‣ StructureimposesinducHvebiasesinthesenetworks
‣ Needtosolveallofthesechallenges:groundlanguageintheworldandleverageinformaHonacrosswholedialogues/documents—otherwisesystemsareinherentlylimited
‣ ScalingtolargerNLPsystems—documentsratherthansentences,booksratherthandocuments
Machine-learnedNLPSystems‣ AggregatelotsofinformaHon
‣ IncreasinglywideuseinvariousapplicaHons/sectors‣ HardtoknowwhycertainpredicHonsaremade
‣Whataretheriskshere?‣ …ofcertainapplicaHons?‣ IE/QA/summarizaHon?‣MT?
‣ …ofmachine-learnedsystems?‣ …ofdeeplearningspecifically?
‣ Dialog?
BiasAmplificaHon‣ Biasindata:67%oftrainingimagesinvolvingcookingarewomen,modelpredicts80%womencookingattestHme—amplifiesbias
Zhaoetal.(2017)
‣ CanweconstrainmodelstoavoidthiswhileachievingthesamepredicHveaccuracy?
‣ PlaceconstraintsonproporHonofpredicHonsthataremenvs.women?
BiasAmplificaHon
Zhaoetal.(2017)
MaximizescoreofpredicHons…
‣ Constraints:malepredicHonraHoonthetestsethastobeclosetotheraHoonthetrainingset
f(y,i)=scoreofpredicHngyonithexample
…subjecttobiasconstraint
BiasAmplificaHon
Rudingeretal.(2018),Zhaoetal.(2018)
‣ Coreference:modelsmakeassumpHonsaboutgendersandmakemistakesasaresult
BiasAmplificaHon
Rudingeretal.(2018),Zhaoetal.(2018)
‣ CanformWinogradschema-liketestsettoinvesHgate
BiasAmplificaHon
Zhaoetal.(2017)
‣ Neuralmodelsactuallyareabitbeveratbeingunbiased,butaresHllskewedbydata
‣ Testsetisbalancedsoaperfectmodelhasfemale%-male%=0(blackline)
BiasAmplificaHon
Alvarez-MelisandJaakkola(2017)
‣ HardertoquanHfythisformachinetranslaHon
‣ “dancer”isassumedtobefemaleinthecontextoftheword“charming”…butmaybethatreflectshowlanguageisused?
Exclusion‣MostofourannotateddataisEnglishdata,especiallynewswire
Codeswitching?
Dialects?
Otherlanguages?(Non-European/CJK)
‣Whatabout:
UnethicalUse‣ SurveillanceapplicaHons?‣ GeneraHngconvincingfakenews/fakecomments?
‣Whatifthesewereundetectable?
DangersofAutomaHcSystems
Slide credit: https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G
‣ “AmazonscrapssecretAIrecruiHngtoolthatshowedbiasagainstwomen”
‣ “Women’sX”organizaHonwasanegaHve-weightfeatureinresumes
‣Women’scollegestoo
‣Wasthisabadmodel?Mayhaveactuallymodeleddownstreamoutcomescorrectly…butthiscanmeanlearninghumans’biases
BadApplicaHons
‣ CreatorsareacHvelymisleadingpeopleintothinkingthisrobothassenHence‣Mostlongerstatementsarescriptedbyhumans‣ “IfIshowthemabeauHfulsmilingrobotface,thentheygetthefeelingthat'AGI'(arHficialgeneralintelligence)mayindeedbenearbyandviable...NoneofthisiswhatIwouldcallAGI,butnorisitsimpletogetworking”
‣ Sophia:“chatbot”thatthecreatorsmakeincredibleclaimsabout
Slidecredit:hvps://themindlist.com/2018/10/12/sophia-modern-marvel-or-mindless-markeHng/
BadApplicaHons‣WangandKosinski:gayvs.straightclassificaHonbasedonfaces
‣ BlogpostbyAgüerayArcas,Todorov,Mitchell:mostlysocialphenomena(glasses,makeup,angleofcamera,facialhair)—badscience,*and*dangerous
‣ Authors:“thisisusefulbecauseitsupportsahypothesis” (physiognomy)
Slidecredit:hvps://medium.com/@blaisea/do-algorithms-reveal-sexual-orientaHon-or-just-expose-our-stereotypes-d998fafdf477
FinalThoughts‣ Youwillfacechoices:whatyouchoosetoworkon,whatcompanyyouchoosetoworkfor,etc.
‣ Techdoesnotexistinavacuum:youcanworkonproblemsthatwillfundamentallymaketheworldabeverplaceoraworseplace(notalwayseasytotell)
‣ AsAIbecomesmorepowerful,thinkaboutwhatweshouldbedoingwithittoimprovesociety,notjustwhatwecandowithit