Diversity matters: Robustness of bias measurements in Wikidata-Reference-Cited by-同舟云学术

Diversity matters: Robustness of bias measurements in Wikidata

Published:2023-04-30 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 15th ACM Web Science Conference 2023
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Author:

Das Paramita¹^ORCID,Karnam Sai Keerthana¹^ORCID,Panda Anirban²^ORCID,Guda Bhanu Prakash Reddy³^ORCID,Sarkar Soumya⁴^ORCID,Mukherjee Animesh¹^ORCID

Affiliation:

1. Computer Science and Engineering, Indian Institute of Technology Kharagpur, India

2. Industrial and Systems Engineering, Indian Institute of Technology Kharagpur, India

3. Computer Science and Engineering, Carnegie Mellon University, USA

4. Microsoft Research, India

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3578503.3583620

Reference35 articles.

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3. Antoine Bordes , Nicolas Usunier , Alberto Garcia-Duran , Jason Weston , and Oksana Yakhnenko . 2013. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems 26 ( 2013 ). Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems 26 (2013).

4. Avishek Bose and William Hamilton . 2019 . Compositional fairness constraints for graph embeddings . In International Conference on Machine Learning. PMLR, 715–724 . Avishek Bose and William Hamilton. 2019. Compositional fairness constraints for graph embeddings. In International Conference on Machine Learning. PMLR, 715–724.

5. Styliani Bourli and Evaggelia Pitoura . 2020 . Bias in Knowledge Graph Embeddings. In 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 6–10 . Styliani Bourli and Evaggelia Pitoura. 2020. Bias in Knowledge Graph Embeddings. In 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 6–10.

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