Unified framework for bias detection and mitigation in classification.
Project description
None are known at this time.
Getting help
If you have any problem with our code or have some suggestions, including the future feature, feel free to contact
- Xudong Han (xudongh1@student.unimelb.edu.au)
or describe it in Issues.
Contributing
We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.
License
This project is distributed under the terms of the APACHE LICENSE, VERSION 2.0. The license applies to all files in the GitHub repository hosting this file.
Acknowledgments
- https://github.com/HanXudong/Decoupling_Adversarial_Training_for_Fair_NLP
- https://github.com/HanXudong/Diverse_Adversaries_for_Mitigating_Bias_in_Training
- https://github.com/SsnL/dataset-distillation
- https://github.com/huggingface/torchMoji
- https://github.com/mhucka/readmine
- https://github.com/yanaiela/demog-text-removal
- https://github.com/lrank/Robust_and_Privacy_preserving_Text_Representations
- https://github.com/yuji-roh/fairbatch
- https://github.com/shauli-ravfogel/nullspace_projection
- https://github.com/AiliAili/contrastive_learning_fair_representations
- https://github.com/AiliAili/Difference_Mean_Fair_Models
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