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

dc.contributor.author	Muyingo, Sylvia
dc.contributor.author	Mbuthia, Michelle
dc.date.accessioned	2023-08-30T10:57:59Z
dc.date.available	2023-08-30T10:57:59Z
dc.date.issued	2022-10
dc.identifier.uri	http://10.176.203.77/handle/123456789/286
dc.description.abstract	Risks 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.language.iso	en_US	en_US
dc.publisher	APHRC	en_US
dc.subject	Data	en_US
dc.subject	Measurement	en_US
dc.subject	Evaluation	en_US
dc.subject	Artificial Intelligence	en_US
dc.subject	Intersectionality	en_US
dc.subject	Checklist	en_US
dc.subject	COVID-19	en_US
dc.title	Incorporating Gender and Intersectionality In Artificial Intelligence (AI) Models and Algorithms	en_US
dc.type	Working Paper	en_US