The Lifecycle of "Facts": A Survey of Social Bias in Knowledge Graphs
This addresses the issue of bias propagation in AI systems for users relying on knowledge graphs, but it is incremental as it surveys existing work rather than introducing new methods.
The paper tackles the problem of social biases in knowledge graphs, which propagate to downstream tasks, by conducting a critical analysis of literature on biases across the knowledge graph lifecycle, discussing limitations in measurement and mitigation strategies and proposing paths forward.
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.