  Knowledge graphs are increasingly used in a plethora of downstream tasks or
in the augmentation of statistical models to improve factuality. However,
social biases are engraved in these representations and propagate downstream.
We conducted a critical analysis of literature concerning biases at different
steps of a knowledge graph lifecycle. We investigated factors introducing bias,
as well as the biases that are rendered by knowledge graphs and their embedded
versions afterward. Limitations of existing measurement and mitigation
strategies are discussed and paths forward are proposed.
