Corpus ID: 208637340

Measuring Social Bias in Knowledge Graph Embeddings

@article{Fisher2019MeasuringSB,
  title={Measuring Social Bias in Knowledge Graph Embeddings},
  author={Joseph Fisher},
  journal={ArXiv},
  year={2019},
  volume={abs/1912.02761}
}
  • Joseph Fisher
  • Published 2019
  • Computer Science
  • ArXiv
  • It has recently been shown that word embeddings encode social biases, with a harmful impact on downstream tasks. However, to this point there has been no similar work done in the field of graph embeddings. We present the first study on social bias in knowledge graph embeddings, and propose a new metric suitable for measuring such bias. We conduct experiments on Wikidata and Freebase, and show that, as with word embeddings, harmful social biases related to professions are encoded in the… CONTINUE READING

    Figures, Tables, and Topics from this paper.

    Citations

    Publications citing this paper.
    SHOWING 1-2 OF 2 CITATIONS

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 32 REFERENCES

    Learning Gender-Neutral Word Embeddings

    VIEW 3 EXCERPTS

    Word embeddings quantify 100 years of gender and ethnic stereotypes

    VIEW 1 EXCERPT