Summary of Fairberts: Erasing Sensitive Information Through Semantic and Fairness-aware Perturbations, by Jinfeng Li et al.
fairBERTs: Erasing Sensitive Information Through Semantic and Fairness-aware Perturbations
by Jinfeng Li, Yuefeng Chen, Xiangyu Liu, Longtao Huang, Rong Zhang, Hui Xue
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Pre-trained language models (PLMs) have transformed natural language processing research and applications, but their encoded biases (e.g., gender and racial discrimination) raise ethical concerns, limiting broader uses. To address these issues, we introduce fairBERTs, a framework for learning fair fine-tuned BERT series models by erasing sensitive information via semantic and fairness-aware perturbations generated by a generative adversarial network. Our experiments on two real-world tasks demonstrate the superiority of fairBERTs in mitigating unfairness while maintaining model utility. We also verify the feasibility of transferring adversarial components to other conventionally trained BERT-like models for fairness improvements. These findings may guide further research on building fairer fine-tuned PLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Language models have made big changes in how we process language, but they can also be unfair and biased. For example, they might treat women or people of color unfairly. To fix this problem, we created a new way to train language models that removes these biases while still making them useful. We tested our idea on two real-world tasks and showed that it works better than other methods. We also found that we can take the good parts of our method and use them to make other language models fairer too. This could help us create more equal and unbiased language models in the future. |
Keywords
» Artificial intelligence » Bert » Generative adversarial network » Natural language processing
