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IvefinishedupafewbooksrecentlyandwouldliketosharereviewsoftwothatIenjoyed。

FirstisCranfordbyElizabethGaskell。

IsawthePBSminiseriesandlikeditenoughtowanttoreadthebook,whichIdidasaninexpensiveKindledownload。

Asanaddedbonus,itisonmy1%Well-ReadChallengelist。

ThenovelisnarratedbyMary,ayoungwomandevotedtothetrueprotagonist,MissMattieJenkyns。

ThereaderbecomesengrossedinthedoingsofthelittleEnglishvillageasitstrugglestoretainitscomfortablemannersinthefaceofimpendingmodernity。

Aquietbook,thenovelgivesaglimpseintothecomplexlayersthatrestbeneaththemannersandfashionsofthetown。

ItwasapleasuretoreadeacheveningbeforeIfellasleep。

ThesecondbookaboutwhichIwanttotellyouisTheWomenbyT。

C。

Boyle。

IreadreviewsofthisandhaddownloadedasampleontheKindle,sowhenIsawitatthelibrary,Isnatcheditup。

Itakerealsatisfactioninbeingthefirstpatrontoreadanewbookatthelibrary。

Ihavenoideawhy;amIaloneinthis?

ThesubjectmatterFrankLloydWrightsscandalousloveaffairsintriguedme。

Who,afterall,doesntloveabitofgossip,evenifitisfictionalized。

ThenovelisnarratedbyTadeshiSato,anAmerican-educatedJapanesearchitectwhopayshandsomelyfortheprivilegeofapprenticingwithWrightatTaliesen。

Tadeshiaimstostickwiththefacts,butheofteninjectshiswryhumor,typicallythroughfootnotes。

ThenoveltracestherolesofWrightswomen,aswellashistreatmentofthemandtheirtreatmentofhim。

ThereaderfirstmeetsOlgivannaLazovichMilanoff,Wrightsthirdwife。

ThesecondpartexaminesMiriamNoel,Wrightsfree-thinking,drug-usingsecondwifewhobecomesbitterasshewatchesWrightrepeathisinfidelitypattern,notwithherthistime,butwithOlgivanna。

ThethirdsectionlooksatMamah,WrightsmistresswhomBoyleportraysasthetrueloveofWrightslife。

EvenasthenovelfocusesonWrightswomen,itmanagestocreatearichworldinwhichthereadergetsarealsenseoftheweightofWrightstransgressions。

Oneverylevel,thisisabookasfinelycraftedasaFrankLloydWrighthouse。

BoyleremovesWrightfromthepedastalonwhichhistalenthasplacedhimandallowsthereadertoseehimasaman。

Hispassionandmoodinessbothenchantandalarm。

Charactersareroundwithadepththatreachesbeyondthebook。

Onasentencelevel,thecomplexityoflanguageandstructureinvitesthereadertolinger,eveninthesaddestmomentsofthebook。

ImaboutathirdofthewaythroughTrainspotting,whichImreadingformybookgroup。

Ifthatwerenotthecase,IthinkIdstop。

IllletyouknowwhatIthinkwhenIfinishit,though。


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The Role Of Technology In Food Sustainability – Business Intelligence Info

Wehumansmakesenseoftheworldbylookingforpatterns,filteringthemthroughwhatwethinkwealreadyknow,andmakingdecisionsaccordingly。

Whenwetalkabouthandingdecisionsofftoartificialintelligence(AI),weexpectittodothesame,onlybetter。

Machinelearningdoes,infact,havethepotentialtobeatremendousforceforgood。

Humansarehinderedbyboththeirunconsciousassumptionsandtheirsimpleinabilitytoprocesshugeamountsofinformation。

AI,ontheotherhand,canbetaughttofilterirrelevanciesoutofthedecision-makingprocess,pluckthemostsuitablecandidatesfromahaystackofrésumés,andguideusbasedonwhatitcalculatesisobjectivelybestratherthansimplywhatwe’vedoneinthepast。

Inotherwords,AIhasthepotentialtohelpusavoidbiasinhiring,operations,customerservice,andthebroaderbusinessandsocialcommunities—anddoingsomakesgoodbusinesssense。

Foronething,eventhemostunintentionaldiscriminationcancostacompanysignificantly,inbothmoneyandbrandequity。

Themerefactofhavingtodefendagainstanaccusationofbiascanlingerlongaftertheissueitselfissettled。

Beyondmanagingriskrelatedtolegalandregulatoryissues,though,there’sabroaderargumentfortacklingbias:inarelentlesslycompetitiveandglobaleconomy,noorganizationcanaffordtoshutitselfofffrombroaderinput,morevariedexperiences,awiderrangeoftalent,andlargerpotentialmarkets。

Thatsaid,thealgorithmsthatdriveAIdon’trevealpure,objectivetruthjustbecausethey’remathematical。

HumansmusttellAIwhattheyconsidersuitable,teachitwhichinformationisrelevant,andindicatethattheoutcomestheyconsiderbest—ethically,legally,and,ofcourse,financially—arethosethatarefreefrombias,consciousorotherwise。

That’stheonlywayAIcanhelpuscreatesystemsthatarefair,moreproductive,andultimatelybetterforbothbusinessandthebroadersociety。

Bias:BadforBusiness

WhenpeopletalkaboutAIandmachinelearning,theyusuallymeanalgorithmsthatlearnovertimeastheyprocesslargedatasets。

Organizationsthathavegatheredvastamountsofdatacanusethesealgorithmstoapplysophisticatedmathematicalmodelingtechniquestoseeiftheresultscanpredictfutureoutcomes,suchasfluctuationsinthepriceofmaterialsortrafficflowsaroundaportfacility。

Computersareideallysuitedtoprocessingthesemassivedatavolumestorevealpatternsandinteractionsthatmighthelporganizationsgetaheadoftheircompetitors。

Aswegathermoretypesandsourcesofdatawithwhichtotrainincreasinglycomplexalgorithms,interestinAIwillbecomeevenmoreintense。

UsingAIforautomateddecisionmakingisbecomingmorecommon,atleastforsimpletasks,suchasrecommendingadditionalproductsatthepointofsalebasedonacustomer’scurrentandpastpurchases。

ThehopeisthatAIwillbeabletotakeontheprocessofmakingincreasinglysophisticateddecisions,suchassuggestingentirelynewmarketswhereacompanycouldbeprofitable,orfindingthemostqualifiedcandidatesforjobsbyhelpingHRlookbeyondtheexpecteddemographics。

AsAItakesontheseincreasinglycomplexdecisions,itcanhelpreducebias,consciousorotherwise。

Byexposingabias,algorithmsallowustolessentheimpactofthatbiasonourdecisionsandactions。

Theyenableustomakedecisionsthatreflectobjectivedatainsteadofuntestedassumptions;theyrevealimbalances;andtheyalertpeopletotheircognitiveblindspotssotheycanmakemoreaccurate,unbiaseddecisions。

Imagine,forexample,amajorcompanythatrealizesthatitspasthiringpracticeswerebiasedagainstwomenandthatwouldbenefitfromhavingmorewomeninitsmanagementpipeline。

AIcanhelpthecompanyanalyzeitspastjobpostingsforgender-biasedlanguage,whichmighthavediscouragedsomeapplicants。

Futurepostingscouldbemoregenderneutral,increasingthenumberoffemaleapplicantswhogetpasttheinitialscreenings。

AIcanalsosupportpeopleinmakingless-biaseddecisions。

Forexample,acompanyisconsideringtwocandidatesforaninfluentialmanagementposition:onemanandonewoman。

Thefinalhiringdecisionlieswithahiringmanagerwho,whentheylearnthatthefemalecandidatehasasmallchildathome,assumesthatshewouldpreferapart-timeschedule。

Thatassumptionmaybewellintentioned,butitrunscountertotheoutcomethecompanyislookingfor。

AnAIcouldapplycorrectivepressurebyremindingthehiringmanagerthatallqualificationsbeingequal,thefemalecandidateisanobjectivelygoodchoicewhomeetsthecompany’scriteria。

Thehopeisthatthehiringmanagerwillrealizetheirunfoundedassumptionandremoveitfromtheirdecision-makingprocess。

Atthesametime,bytrackingthepatternofhiringdecisionsthismanagermakes,theAIcouldalertthem—andotherpeopleinHR—thatthecompanystillhassomeremaininghiddenbiasesagainstfemalecandidatestoaddress。

LookforWhereBiasAlreadyExists

Inotherwords,ifwewantAItocountertheeffectsofabiasedworld,wehavetobeginbyacknowledgingthattheworldisbiased。

Andthatstartsinasurprisinglylow-techspot:identifyinganybiasesbakedintoyourownorganization’scurrentprocesses。

Fromthere,youcandeterminehowtoaddressthosebiasesandimproveoutcomes。

TherearemanyscenarioswherehumanscancollaboratewithAItopreventorevenreversebias,saysJasonBaldridge,aformerassociateprofessorofcomputationallinguisticsattheUniversityofTexasatAustinandnowco-founderofPeoplePattern,astartupforpredictivedemographicsusingsocialmediaanalytics。

Inthehighlyregulatedfinancialservicesindustry,forexample,Baldridgesaysbanksarerequiredtoensurethattheiralgorithmicchoicesarenotbasedoninputvariablesthatcorrelatewithprotecteddemographicvariables(likeraceandgender)。

Thebanksalsohavetoprovetoregulatorsthattheirmathematicalmodelsdon’tfocusonpatternsthatdisfavorspecificdemographicgroups,hesays。

What’smore,theyhavetoallowoutsidedatascientiststoassesstheirmodelsforcodeordatathatmighthaveadiscriminatoryeffect。

Asaresult,banksaremoreevenhandedintheirlending。

CodeIsOnlyHuman

Thereasonforthesechecksandbalancesisclear:thealgorithmsthatdriveAIarebuiltbyhumans,andhumanschoosethedatawithwhichtoshapeandtraintheresultingmodels。

Becausehumansarepronetobias,wehavetobecarefulthatweareneithersimplyconfirmingexistingbiasesnorintroducingnewoneswhenwedevelopAImodelsandfeedthemdata。

“Fromtheperspectiveofabusinessleaderwhowantstodotherightthing,it’sadesignquestion,”saysCathyO’Neil,whosebest-sellingbookWeaponsofMathDestructionwaslong-listedforthe2016NationalBookAward。

“Youwouldn’tletyourcompanydesignacarandsenditoutintheworldwithoutknowingwhetherit’ssafe。

Youhavetodesignitwithsafetystandardsinmind,”shesays。

“Bythesametoken,algorithmshavetobedesignedwithfairnessandlegalityinmind,withstandardsthatareunderstandabletoeveryone,fromthebusinessleadertothepeoplebeingscored。

”(TolearnmorefromO’Neilabouttransparencyinalgorithms,readThinkersinthisissue。

)

Don’tDoWhatYou’veAlwaysDone

Toeliminatebias,youmustfirstmakesurethatthedatayou’reusingtotrainthealgorithmisitselffreeofbias,or,rather,thatthealgorithmcanrecognizebiasinthatdataandbringthebiastoahuman’sattention。

SAPhasbeenworkingonaninitiativethattacklesthisissuedirectlybyspottingandcategorizinggenderedterminologyinoldjobpostings。

NothingasovertasNowomenneedapply,whicheveryoneknowsisdiscriminatory,butphraseslikeoutspokenandaggressivelypursuingopportunities,whichareproventoattractmalejobapplicantsandrepelfemaleapplicants,andwordslikecaringandflexible,whichdotheopposite。

Oncehumanscategorizethislanguageandfeeditintoanalgorithm,theAIcanlearntoflagwordsthatimplybiasandsuggestgender-neutralalternatives。

Unfortunately,thisde-biasingprocesscurrentlyrequirestoomuchhumaninterventiontoscaleeasily,butastheamountofavailablede-biaseddatagrows,thiswillbecomefarlessofalimitationindevelopingAIforHR。

Similarly,companiesshouldlookforspecificityinhowtheiralgorithmssearchfornewtalent。

AccordingtoO’Neil,there’snoone-size-fits-alldefinitionofthebestengineer;there’sonlythebestengineerforaparticularroleorprojectataparticulartime。

That’stheneedleinthehaystackthatAIiswellsuitedtofind。

LookBeyondtheObvious

AIcouldbeinvaluableinradicallyreducingdeliberateandunconsciousdiscriminationintheworkplace。

However,themoredatayourcompanyanalyzes,themorelikelyitisthatyouwilldealwithstereotypes,O’Neilsays。

Ifyou’relookingformathprofessors,forexample,andyouloadyourhiringalgorithmwithallthedatayoucanfindaboutmathprofessors,youralgorithmmaygivealowerscoretoablackfemalecandidatelivinginHarlemsimplybecausetherearefewerblackfemalemathematiciansinyourdataset。

ButifthatcandidatehasaPhDinmathfromCornell,andifyou’vetrainedyourAItoprioritizethatcriterion,thealgorithmwillbumpherupthelistofcandidatesratherthansummarilyrulingoutapotentiallyhigh-valuehireonthespuriousbasisofraceandgender。

TofurtherimprovetheoddsthatAIwillbeuseful,companieshavetogobeyondspottingrelationshipsbetweendataandtheoutcomestheycareabout。

Itdoesn’ttakesophisticatedpredictivemodelingtodetermine,forexample,thatwomenaredisproportionatelylikelytojumpoffthecorporateladderatthehalfwaypointbecausethey’restrugglingwithwork/lifebalance。

Manycompaniesfinditalltooeasytoconcludethatwomensimplyaren’tqualifiedformiddlemanagement。

However,acompanycommittedtosmarttalentmanagementwillinsteadaskwhatitisaboutthesepositionsthatmakesthemincompatiblewithwomen’slives。

Itwillthenexplorewhatitcanchangesothatitdoesn’tlosetalentandinstitutionalknowledgethatwillcostthecompanyfarmoretoreplacethantoretain。

Thatcompanymayevenapplyasecondlayerofmachinelearningthatlooksatitsownsuggestionsandmakesfurtherrecommendations:“Itlookslikeyou’retryingtodoX,soconsiderdoingY,”whereXmightbepromotingmorewomen,makingtheworkforcemoreethnicallydiverse,orimprovingretentionstatistics,andYisredefiningjobresponsibilitieswithgreaterflexibility,hostingrecruitingeventsincommunitiesofcolor,orredesigningbenefitspackagesbasedonwhatsimilarcompaniesoffer。

ContextMatters—andContextChanges

EventhoughAIlearns—andmaybebecauseitlearns—itcanneverbeconsidered“setitandforgetit”technology。

Toremainbothaccurateandrelevant,ithastobecontinuallytrainedtoaccountforchangesinthemarket,yourcompany’sneeds,andthedataitself。

Sourcesforlanguageanalysis,forexample,tendtobebiasedtowardstandardAmericanEnglish,soifyou’rebuildingmodelstoanalyzesocialmediapostsorconversationallanguageinput,Baldridgesays,youhavetomakeadeliberateefforttoincludeandcorrectforslangandnonstandarddialects。

StandardEnglishappliesthewordsicktosomeonehavinghealthproblems,butit’salsoapopularslangtermforsomethinggoodorimpressive,whichcouldleadtoanawkwardexperienceifsomeoneconfusesthetwomeanings,tosaytheleast。

Correctingforthat,oraddingmorerulestothealgorithm,suchas“Thewordsickappearsinproximitytopositiveemoji,”takeshumanoversight。

MovingForwardwithAI

Today,AIexcelsatmakingbiaseddataobvious,butthatisn’tthe?

sameaseliminatingit。

It’suptohumanbeingstopayattentionto?

theexistenceofbiasandenlistAItohelpavoidit。

ThatgoesbeyondsimplyimplementingAItoinsistingthatitmeetbenchmarksforpositiveimpact?

Thebusinessbenefitsoftakingthisstepare—orsoonwillbe—obvious。

InIDCFutureScapes’webcast“WorldwideBigData,BusinessAnalytics,andCognitiveSoftware2017Predictions,”researchdirectorDavidSchubmehlpredictedthatby2020perceivedbiasandlackofevidentiarytransparencyincognitive/AIsolutionswillcreateanactivistbacklashmovement,withupto10%ofusersbackingawayfromthetechnology。

However,SchubmehlalsospeculatedthatconsumerandenterpriseusersofmachinelearningwillbefarmorelikelytotrustAI’srecommendationsanddecisionsiftheyunderstandhowthoserecommendationsanddecisionsaremade。

Thatmeansknowingwhatgoesintothealgorithms,howtheyarriveattheirconclusions,andwhethertheydeliverdesiredoutcomesthatarealsolegallyandethicallyfair。

Clearly,organizationsthatcanaddressthisconcernexplicitlywillhaveacompetitiveadvantage,butsimplystatingtheircommitmenttousingAIforgoodmaynotbeenough。

TheyalsomaywishtosupportacademiceffortstoresearchAIandbias,suchastheannualFairness,Accountability,andTransparencyinMachineLearning(FATML)workshop,whichwasheldforthethirdtimeinNovember2016。

O’Neil,whoblogsaboutdatascienceandfoundedtheLedeProgramforDataJournalism,anintensivecertificationprogramatColumbiaUniversity,isgoingonestepfurther。

Sheisattemptingtocreateanentirelynewindustrydedicatedtoauditingandmonitoringalgorithmstoensurethattheynotonlyrevealbiasbutactivelyeliminateit。

Sheproposestheformationofgroupsofdatascientiststhatevaluatesupplychainsforsignsofforcedlabor,connectchildrenatriskofabusewithresourcestosupporttheirfamilies,oralertpeoplethroughasmartphoneappwhentheircreditscoresareusedtoevaluateeligibilityforsomethingotherthanaloan。

AswebegintoentrustAIwithmorecomplexandconsequentialdecisions,organizationsmayalsowanttobeproactiveaboutensuringthattheiralgorithmsdogood—sothattheircompaniescanuseAI?

todowell。

D!

Readmorethoughtprovokingarticlesinthe?

latestissueofthe?

DigitalistMagazine,ExecutiveQuarterly。

AbouttheAuthors:

YvonneBaurisHeadofPredictiveAnalyticsforSapSuccessFactorssolutions。

BrendaReidisVicePresidentofProductManagementforSapSuccessFactorssolutions。

SteveHuntisSeniorVicePresidentofHumanCapitalManagementResearchforSapSuccessFactorssolutions。

FawnFitterisafreelancewriterspecializinginbusinessandtechnology。

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