Mohammad Alothman: How AI and Humans Contribute to AI Bias

1. MohammadAlothman:HowAIand HumansContributetoAIBias IamMohammadAlothman,asophisticatedexpertinartificial intelligencetechnologysolutions,havingspentthebestyearsofmy studylifeunderstandingthenuances of artificialintelligenceandits applications. Butthemostcomplexquestionthat reallybaffledmethroughoutmy careeristhequestionregardingbias inAIsystems. Towhat extent isAIaresultof humanbias?Canoneattributewrongdecision- makingbyAIsystemstobiasesofthedesigners,gatherers ofdata, andalgorithmbuilders? Theseissues carry immenseimportanceforthefutureofAIas well asitsintegrationintosociety.Iwould thentakethe relationship betweenAIandhumans,especiallytherolehumanbiasplayswhenformingsystemsAI may or maynotbeaddressingor atleastattemptingtoreducesome. ThefieldofAIdesignhascomealongway;thishasreallymadesomeoutstanding innovation possible, suchaschangingdomainsfromhealthandfinancetoothers;nameafew.Yet,the liabilityofAIbiasis muchmoreapparentasAItechnologysolutionsaremoreandmore integratedintooureverydaylives. BiasinAIagentscanamplifydiscrimination,inequity,andunfairness,whichcanresultintoxic effects.Throughoutmylearning throughout mycareer,understanding rootcausesofAI bias helpsinensuringthatAIandhumanstogethercanwork inwaysthataddbenefits toallparties concerned. UnderstandingAIBiasandItsOrigins Ultimately,AIbiashas its rootsinthedatathatit is trainedon. Ifhumanbiasesfeedintotheinformationusedfortrainingtheartificialintelligencesystems, thenbydefinition, thatfeedsintotheAIalso.That'samassiveproblembecause, in effect, it meansthe nature oftheseAI systemsisintrinsicallyproblematic-beingimpartial, data-driven and,hence,decision-makingby objectivemetric.

2. Yet,ifthetrainingdataon whichAIsystemsareorganizedcontainbiasedhistoricaldataorare biasedbasedonthestereotypingofthepeoplewhogatheredandannotatedit,anoutputof the biascontainedinthosedatawillbemadeby theAIsystem. • FrommyexperienceworkingwithAITechSolutions,I’veseen thatbiasesoften creepinto AI systemsinthreemainareas: • Datacollection:Thebiases inAIcanalsobefoundthroughbiases indatacollection. Therearehumanbiasespertainingtohowdataisbeingcollectedorannotatedordealt with.Therefore,theresultswillbebiased asaresult.Forexample,if anAIsystem makessomepredictionsaboutfuturerecidivismamongoffendersbasedontheir prior crimerecords, racialandsocio-economicbiasmaygetprolongedandperpetuated because these thingsmaybeembedded inthedatadue to social inequalities. • AlgorithmDesign:Bias,however,canalsobebroughtbythedesigningprocessofAI algorithms,butdevelopers' traininghasshownnottobeimmunisedtomaking their choicesfromthe verydesigning procedureto introduce thebiasbywayof designingan outcome-preferringratherthananeutralistic outcomeone,andusually,atthecorelevel ofdesigningaresuchbiasesinherentand tendtoprofoundlyinfluencewhatkind of decisionsanAIwillbeundertaking. • Decision-MakingProcesses: OnceAIsystemsarebuilt,thesesystems relyon algorithms tomakedecisions.Whenthealgorithms arebiasedandbuiltfromfaultyAI designs,theymightevenfurtherentrenchbiasedresultsincriticalareas suchashiring, criminaljustice,orhealthcare. • Ihavecometorealizethroughthisexperienceofworking intheareaof AIdevelopmentthat areasjustliketheseresultinthebiasesofAI; prettymuchevidentthat thishastranscended beyondjustathingandauser;therelationship, dynamicsbetweenAIandhumanthathuman biases inherentlyaffecttheseAIsystems.

3. TheRelationshipofAIandHumanBias ThemoreIlearnabouttheAIworld,thecleareritgets:atits very core,biasis human.Inmany ways,AIandhumanbiasbringsitselftolifeinlittleprejudicesfromthedatathatwecollectall thewaythroughthedecisionsindesigningcreatedbydevelopers who are,forthemostpart, unawareoftheimpacttheirdecisionsmight carry. Bias,initspurestform,isnotnecessarily intentional. Thiscanbeunconsciousorstructural,based onculturalvaluesandembeddedexperiences. Forinstance,anAIsystemusedinhiringistrainedondatathat consistsmainlyof male applicants,so,initsrecommendations, itmayfavormale candidatesunconsciouslybecauseit wasnotexplicitlyprogrammed to doso. This is adirectimplicationoftheinherenthumanbiasinthehuman-madedatasetandquite clearlyshowshowAIandhumansmustalwaysbeinextricablylinked.Ipersonallyhaveseen, in myowncareer,howbiasesinhumandatacollectionaswellasinAIdesigncan leadto discriminatoryresults. Perhapstheworst thingaboutAIbiasis thatitoccursandcontinuesonforalongtime before beingnoticed. The case of human decision-makingentitiesmayatleastprovideexplanationofreasonsfor decisions made;ontheother hand,AIandhumansystemshavebeendescribedasa"black box,"whereinitisunclearastowhyadecisionwasarrivedat.Thisvaguenesshaslaidonthe morecriticalnecessityofaddressingroot causesofbiasin AIsystems.

4. MitigatingBias inAISystems:StrategiestoAddressAIBias AsAIcontinues toadvance,thereisanever-increasingneedtoaddressAIbias.Workingwith AITechSolutionshasmademeidentifyseveralstrategiesthatcanhelpcurbbiasinAI systems.Theseapproachesareaimedatenhancingthe datausedforthe trainingof AI, refinementofalgorithms,andmaintenanceoftransparencyandfairness inthedecision-making process. ImprovingDataCollectionandCuration Theonlypracticalmeansbywhich AIbiaswillbeovercome isbyworkingtoimprove the methodfordatagatheringandpreparation. ThedevelopersneedtomakesurethatthedatacreatedinanavailablemannerforAItraining needstobediverseaswellasrepresentativewithoutanybias-baseddata. It wouldpossibly seekdatafromanunderrepresentedgrouppreviouslyortransformhowdataarelabelled withoutdevelopingfurtherbasedbiases. Ihave learnedfromworking withAITechSolutionsthatthebestwaytoachievemorecorrect andfair AIsystemsisthroughtheemploymentofdiverselyrepresentative datasets.For example,inhealthcare,datadiversitycanavoidthepitfallofsystematicallyprejudicing certain racesorethnicgroupswithbiased adviceonmedicalmatters. TransparentAlgorithmDesign Ofcourse, transparencyisoneof themain issuesinthematterof AIbias. Fordevelopersto successfully makealgorithms,theyare requiredtofocusondevelopmentalgorithmsthat are efficient yettransparent. WhenAIsystemsaremoreexplainabletohumanusers,thereobviouslyisgreaterscopefor usersunderstandingthereasons behinddecisions, andtothatextentdiscoveringandcorrecting biaseswithin anAIsystem. As anAIdesigner comingfrom thetrenches,Iamconvincedthatweneedopen-source frameworksthatencouragepeerreviewand community-drivencollaboration.Themore collaborativedevelopersthinkaboutbiasesofalgorithms,the easieritwillbetorefine and updatethosesystemsovertime. RegularAuditsandAccountability Forthetimebeing,atleast,periodic checksontheAIsystems havetobedonetoovercomethe problem ofAIbias.

5. ResultsfromtheAIsystemneedtobetestedonwhetherthoseshowbiasedcharacteristicsin decidingvarioustypesofissuesbeforeit.Thattypeofperiodiccheckingwillhelpensure the result throughAIsystemsandinthefinalissues avoidmuchbiasmainlywithcontinuous updatesandthen retrainthemwithnewinformation duein course. PeriodicreviewofAItechsolutionsbyone'sexperienceoften revealssome inadvertentbiases thatcouldcropinasAIsystemsprogressandevolve.Thisapproachensures thattheAI systemsarenotcompromisedintheirintegrityandthesystemsdonotharmbutremain effectiveincarryingouttheirunderpinningfunctions. 4.Ethical guidelinesandhumanoversight TheAIsystemsrequireanappropriateethicalframeworkandhuman review.Developersand teamsmustalsodevelop structuresbasedonfairness,equity,andtransparencyinthecreation andimplementationofAI. Mostimportantly,humanoversight playsamuchmorecriticalrole inthedecision-making process,even incriticalfieldslike criminaljusticeand employment,whereAIcanlead tobiased decisionswithsignificantimplications. Workingattechfirms andinsideAIcompanies, ItendtobelievethatAI,whateveritsfuturemay hold,alwaysneedstobethoughtofasacomplementofhuman decision-makingratherthanits replacement.Humanshaveto stayinvolvedsoAImaybeused responsiblyandethically.

6. Conclusion:TheWayAhead The connection oftheAIandhumanworld iscomplexand varied.No matterhowsophisticated theseAIsystemsarewithpowerful tool-givingrisetoinnovationandefficiency, by theirvery nature,theyrelyonhumanbiasatmanyjunctures-fromtheverydatacollectionprocessthrough theformulationofalgorithmsandactualdecision-making. Itbecomesveryimportant tounderstandandappreciate that AIbiasoriginatesmuchmorein thecontextofhumanitythanis generallyenvisaged. Throughmy work inAITechSolutions,Ihaveunderstoodthefactthatbias inAIcallsfor interventionatadifferentlevel.Betterwaysfordatacollection,betterAIdesignprocess transparencyofalgorithms,periodaudits,andhumanoversightcan aid movingtowardefficient andfairsystemsbyAI. Somethingweare buildingin termsoftechnologyandhowourbiasesshape itissomething to nowwatchoutfor.Onlythen willitbe possible wheneveryone isinconsciousactionand everyoneis workingtogethertocreatesystemsforgenuineAIbettersuitedandbeneficial fortheoverallinterestsof allhumanbeingsandnotrepeatingtheinequalitiesweseehappeningin thepast. AbouttheAuthor,MohammadAlothman MohammadAlothmanisa leaderinartificialintelligenceand AITechSolutionswithan interest intheethical designandimplementationofAItechnologies. WithexperienceinAIdesignanddevelopmentthroughyears,Mohammad Alothmanhas focusedhiscareerontheconstructionof systemsthat valuefairness,transparency,and accountability. MohammadAlothman’sresearchhasparticularlytouchedareasincustomerservicefor businessesandishelpfulespeciallyinthedevelopmentofresponsible AIpracticesthatdiminish biasandoptimizesolutionstowardbenefitingall.