CS388:NaturalLanguageProcessingLecture26:Wrapup+Ethics

GregDurrett

Administrivia

‣ FinalprojectsdueDecember14

‣ ProjectpresentaHonsnextTuesdayandThursday

‣ PleasefilloutthecourseevaluaHonifyouhaven’talready!Timeattheendofclasstoday

ThisLecture‣ Courserecap

‣ EthicsinNLP

StructureinNLP

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…

�e

�t

‣ Candolanguageanalysiswithsequencemodels

SequenHalStructure:GeneraHon

themoviewasgreat

le

<s>

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:Analysis

themoviewasgreat

Leietal.(2017)

trees

trees

notrees

notrees

???

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

EthicsinNLP—whatcangowrong?

Whatcanactuallygowrong?

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

Zhaoetal.(2017)

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

Slidecredit:TheVerge

DangersofAutomaHcSystems

Slidecredit:allout.org

‣ Offensiveterms

DangersofAutomaHcSystems

Slidecredit:SamBowman

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

`

‣ FacecepHon:computaHonalphrenology…hvp://www.facecepHon.com

FinalThoughts‣ Youwillfacechoices:whatyouchoosetoworkon,whatcompanyyouchoosetoworkfor,etc.

‣ Techdoesnotexistinavacuum:youcanworkonproblemsthatwillfundamentallymaketheworldabeverplaceoraworseplace(notalwayseasytotell)

‣ AsAIbecomesmorepowerful,thinkaboutwhatweshouldbedoingwithittoimprovesociety,notjustwhatwecandowithit