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Knowledge graphs are increasingly used in a plethora of downstream tasks orin 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 differentsteps of a knowledge graph lifecycle. We investigated factors introducing bias,as well as the biases that are rendered by knowledge graphs and their embeddedversions afterward. Limitations of existing measurement and mitigationstrategies are discussed and paths forward are proposed.

The Lifecycle of "Facts": A Survey of Social Bias in Knowledge Graphs