Data is increasingly important in planning and decision-making, but data can also be biased and shaped by our assumptions and gaps in knowledge. When this gap-filled data is plugged into algorithms, it can amplify existing forms of discrimination. For example, the global HIV response is being undermined by the fact that many governments deny the existence of the key populations …
