Algorithmic bias in public health AI: a silent threat to equity in low-resource settings

Injustices-inequities in access to care or discriminatory health policy, say-are embedded within datasets for learning (9,10). Representation bias is present when samples from urban, wealthy, or connected groups lead to the ignoring of samples from rural, indigenous, or disenfranchised groups (2). In addition, measurement bias is present when health endpoints are approximated with the help of proxy variables-hospital attendance or smartphone usage, say-strikingly different between socioeconomic or even cultural environments (11). These biases are again compounded by aggregation bias, by which models assume homogeneity between heterogeneous groups, and deployment bias, by which tools developed within…