2024
A FAIR, open-source virtual reality platform for dendritic spine analysis
Reimer M, Kauer S, Benson C, King J, Patwa S, Feng S, Estacion M, Bangalore L, Waxman S, Tan A. A FAIR, open-source virtual reality platform for dendritic spine analysis. Patterns 2024, 5: 101041. PMID: 39568639, PMCID: PMC11573899, DOI: 10.1016/j.patter.2024.101041.Peer-Reviewed Original ResearchVirtual realityVirtual reality platformSoftware ecosystemReality platformData standardSuperior accuracyDatasetWorkflowValidation processDendritic spine morphologySpine analysisDendritic spinesReconstruction techniqueSpine lengthMethod's superior accuracyDendritic spine lengthSpine morphologyMetricsMorphological metricsNeurodataFairnessExplainable AI for Fair Sepsis Mortality Predictive Model
Chang C, Wang X, Yang C. Explainable AI for Fair Sepsis Mortality Predictive Model. Lecture Notes In Computer Science 2024, 14845: 267-276. DOI: 10.1007/978-3-031-66535-6_29.Peer-Reviewed Original ResearchTransfer learning processFairness of model predictionsExplainable AIExplainability methodsAI applicationsPrediction modelArtificial intelligenceImportance algorithmTransform clinical decision-makingExplainabilityDiverse patient demographicsFairnessHealthcare stakeholdersPredictive performanceLearning processMitigate biasAlgorithmHealthcare deliveryDecision-makingIntelligenceClinical decision-makingHealthcare professionalsMortality prediction modelMethodHealthcareAn ExplainableFair Framework for Prediction of Substance Use Disorder Treatment Completion
Lucas M, Wang X, Chang C, Yang C, Braughton J, Ngo Q. An ExplainableFair Framework for Prediction of Substance Use Disorder Treatment Completion. 2024, 00: 157-166. DOI: 10.1109/ichi61247.2024.00028.Peer-Reviewed Original ResearchSensitive attributesMachine learning modelsIn-process approachLearning modelsResource allocationFairness frameworkFairnessEnhancement processSubstance use disorder treatment completionLikelihood of adoptionClinical decision-makingFrameworkHealthcare providersLevels of governmentModel changesExplainabilityDecision-makingTreatment completionTrustworthinessHealthcareMute by Visitation of God, Competency to Stand Trial and Fitness to Plead.
Buchanan A. Mute by Visitation of God, Competency to Stand Trial and Fitness to Plead. The Journal Of The American Academy Of Psychiatry And The Law 2024, 52: 207-215. PMID: 38834365, DOI: 10.29158/jaapl.240005-24.Peer-Reviewed Original ResearchConceptsVisitation of GodFairness of criminal proceedingsEnglish legal termsCriminal proceedingsLegal commentaryLegal termsEnglish attemptsRelevant historical backgroundDefendantHistorical backgroundHistorical exegesisOrigins of competenceGodPleadingsLawProceedingsCompetenceDignityFairnessCommentaryExegesisConcernsPeopleRegulating Explainability in Machine Learning Applications -- Observations from a Policy Design Experiment
Nahar N, Rowlett J, Bray M, Omar Z, Papademetris X, Menon A, Kästner C. Regulating Explainability in Machine Learning Applications -- Observations from a Policy Design Experiment. 2024, 2101-2112. DOI: 10.1145/3630106.3659028.Peer-Reviewed Original ResearchAI applicationsArtificial intelligenceEU General Data Protection RegulationGeneral Data Protection RegulationMachine learning applicationsData Protection RegulationHuman subject studiesAI-onlyLearning applicationsProtection RegulationExplainabilityExecution orderEU AI ActAI ActContinuous collaborationDesign processEvidence of compliancePrivacyApplicationsSecurityIntelligenceMachineIterationFairnessTeam of researchersA scoping review of fair machine learning techniques when using real-world data
Huang Y, Guo J, Chen W, Lin H, Tang H, Wang F, Xu H, Bian J. A scoping review of fair machine learning techniques when using real-world data. Journal Of Biomedical Informatics 2024, 151: 104622. PMID: 38452862, PMCID: PMC11146346, DOI: 10.1016/j.jbi.2024.104622.Peer-Reviewed Original ResearchConceptsReal-world dataHealth care applicationsHealth care domainMachine learningArtificial intelligenceCare applicationsMulti-modal dataIntegration of artificial intelligenceMachine learning techniquesPre-processing techniquesCare domainBias mitigation approachesPublic datasetsAI/ML modelsModel fairnessLearning techniquesOptimal fairnessHealth care dataAI toolsHealth careAlgorithmic biasML modelsAI/MLFairnessBias issuesAchieving Equity via Transfer Learning With Fairness Optimization
Wang X, Chang C, Yang C. Achieving Equity via Transfer Learning With Fairness Optimization. IEEE Access 2024, 12: 195229-195241. DOI: 10.1109/access.2024.3519465.Peer-Reviewed Original ResearchBias mitigation approachesFairness optimizationTransfer learningReal-world datasetsMachine learning algorithmsMachine learning modelsDecision-making systemMinimal performance degradationFairness enhancementFairness constraintsAccurate classifierLearning algorithmsAI systemsTraining processMitigation approachesLearning modelsTrade-OffsPerformance degradationPerformance impactSuperior fairnessPerformance optimizationFairnessPredictive performanceLearningMachineBias and Fairness in Chatbots: An Overview
Xue J, Wang Y, Wei C, Liu X, Woo J, Kuo C. Bias and Fairness in Chatbots: An Overview. APSIPA Transactions On Signal And Information Processing 2024, 13: e102. DOI: 10.1561/116.00000064.Peer-Reviewed Original Research
2020
Disability Rights as a Necessary Framework for Crisis Standards of Care and the Future of Health Care
Guidry-Grimes L, Savin K, Stramondo J, Reynolds J, Tsaplina M, Burke T, Ballantyne A, Kittay E, Stahl D, Scully J, Garland-Thomson R, Tarzian A, Dorfman D, Fins J. Disability Rights as a Necessary Framework for Crisis Standards of Care and the Future of Health Care. The Hastings Center Report 2020, 50: 28-32. PMID: 32596899, DOI: 10.1002/hast.1128.Peer-Reviewed Original ResearchConceptsCrisis standards of careDisability justiceCrisis standardsInclusion of disabled peopleJusticeRecognition justiceStandard of careEquitable processDisabled peopleDecision-makingInstitute of MedicineMovement demandsCrisisHealth careLawVision statementsProvider engagementFairnessCareCOVID-19InterestCrisis planningCommitmentDisaster situationsDisability
2019
Using Face Recognition to Detect “Ghost Writer” Cheating in Examination
He H, Zheng Q, Li R, Dong B. Using Face Recognition to Detect “Ghost Writer” Cheating in Examination. Lecture Notes In Computer Science 2019, 11462: 389-397. DOI: 10.1007/978-3-030-23712-7_54.Peer-Reviewed Original ResearchPublic cloud servicesFace recognition servicesFeature extraction moduleFace recognition frameworkOpen source projectsFace recognition techniquesAccuracy of detectionCloud servicesRecognition serviceRecognition frameworkExtraction moduleSource projectsPrototype systemRecognition techniquesFace recognitionLayer architecturePublic dataOnline distance educationDistance educationServicesStudent identificationFrameworkArchitectureSystemFairness
2012
Reconstruing Intolerance
Luguri J, Napier J, Dovidio J. Reconstruing Intolerance. Psychological Science 2012, 23: 756-763. PMID: 22653799, DOI: 10.1177/0956797611433877.Peer-Reviewed Original Research
1991
Do physicians have an ethical obligation to care for patients with AIDS?
Angoff NR. Do physicians have an ethical obligation to care for patients with AIDS? The Yale Journal Of Biology And Medicine 1991, 64: 207-46. PMID: 1788990, PMCID: PMC2589324.Peer-Reviewed Original ResearchMeSH KeywordsAcquired Immunodeficiency SyndromeAmerican Medical AssociationCodes of EthicsContractsEthics, MedicalFearGovernment RegulationHippocratic OathHistory, 20th CenturyHumansLegislation, MedicalMoral ObligationsPatient Care PlanningPhilosophy, MedicalPhysician-Patient RelationsPrejudiceRefusal to TreatSocial ResponsibilityUnited StatesVirtuesConceptsPractice of medicineEthical obligationEthical idealPhysicians' ethical obligationsEthical codesMedical ethicistsGood physicianPolitical milieuObligationsMoralityQuestionsPracticeEthicistsImmunodeficiency syndromeIdealWritingAppealMedicinePatientsPhysiciansCaringAIDSLawPhysician capacityFairness
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