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

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.