Summary of Leveraging Prototypical Representations For Mitigating Social Bias Without Demographic Information, by Shadi Iskander et al.
Leveraging Prototypical Representations for Mitigating Social Bias without Demographic Information
by Shadi Iskander, Kira Radinsky, Yonatan Belinkov
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 The novel approach presented in this paper, DAFair, tackles social bias in language models without relying on explicit demographic labels. Instead, it utilizes predefined prototypical demographic texts and a regularization term during fine-tuning to mitigate bias in the model’s representations. The paper demonstrates the effectiveness of DAFair across two tasks and two models compared to previous approaches that don’t rely on labeled data. Furthermore, it outperforms common debiasing methods when working with limited demographic-annotated data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about finding ways to reduce social biases in language models. Right now, many language models can be biased towards certain groups of people based on the data they were trained on. The authors of this paper want to change that by creating a new way to make language models more fair and unbiased. They don’t need any special labels or information about the data – just some examples of what different demographic groups would say or write. By using these examples, they can fine-tune the language model to be less biased. The results show that this approach works better than others in certain situations. |
Keywords
* Artificial intelligence * Fine tuning * Language model * Regularization
