Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models-Reference-Cited by-同舟云学术

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Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models

Published:2022 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022)
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Author:

Balkir Esma,Kiritchenko Svetlana,Nejadgholi Isar,Fraser Kathleen

Publisher

Association for Computational Linguistics

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Fairness-aware Classifier Design via Multi-objective Fuzzy Genetics-based Machine Learning;2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE);2024-06-30

2. Understanding the Dynamics of the Evolution of Weights in Neural Networks using Dynamic Mode Decomposition Approach;2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI);2024-05-09

3. Policy advice and best practices on bias and fairness in AI;Ethics and Information Technology;2024-04-29

4. A survey of explainable AI techniques for detection of fake news and hate speech on social media platforms;Journal of Computational Social Science;2024-03-15

5. Toward explainable AI (XAI) for mental health detection based on language behavior;Frontiers in Psychiatry;2023-12-07

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