Abstract
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.
| Originalsprache | Englisch |
|---|---|
| Titel | Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing : Volume 1: Long Papers |
| Redakteure/-innen | Yulan He, Heng Ji, Sujian Li, Yang Liu, Chua-Hui Chang |
| Seitenumfang | 14 |
| Band | 1 |
| Erscheinungsort | Stroudsburg |
| Herausgeber (Verlag) | Association for Computational Linguistics (ACL) |
| Erscheinungsdatum | 01.11.2022 |
| Seiten | 639-652 |
| ISBN (elektronisch) | 978-1-955917-65-0 |
| DOIs | |
| Publikationsstatus | Erschienen - 01.11.2022 |
| Extern publiziert | Ja |
| Veranstaltung | Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics - online Dauer: 20.11.2022 → 23.11.2022 |
Bibliographische Notiz
Publisher Copyright:© 2022 Association for Computational Linguistics.
Fachgebiete und Schlagwörter
- Informatik
ASJC Scopus Sachgebiete
- Theoretische Informatik und Mathematik
- Angewandte Informatik
- Information systems
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Interview with AIhub on Social Bias in Knowledge Graphs
16.11.22
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