Incorporating Gender and Intersectionality In Artificial Intelligence (AI) Models and Algorithms

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dc.contributor.authorMuyingo, Sylvia
dc.contributor.authorMbuthia, Michelle
dc.date.accessioned2023-08-30T10:57:59Z
dc.date.available2023-08-30T10:57:59Z
dc.date.issued2022-10
dc.description.abstractRisks of harm from the multiple and overlapping crises related to COVID-19 vary based upon one’s gender, age (children, adolescents and elderly), level of education, occupation, geographical location (urban, rural, informal settlements, urban slums, camps), marital status (married, single, widowed), ethnicity/race, economic status, religion, disability (physical mobility, albinism, hearing. disability). Depending on these identities, circumstances and characteristics, people experience differing risks of contracting and/or accessing needed information and services related to the prevention, treatment and care of COVID-19.en_US
dc.identifier.urihttp://10.176.203.77/handle/123456789/286
dc.language.isoen_USen_US
dc.publisherAPHRCen_US
dc.subjectDataen_US
dc.subjectMeasurementen_US
dc.subjectEvaluationen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectIntersectionalityen_US
dc.subjectChecklisten_US
dc.subjectCOVID-19en_US
dc.titleIncorporating Gender and Intersectionality In Artificial Intelligence (AI) Models and Algorithmsen_US
dc.typeWorking Paperen_US

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