Summary of Bias Mitigation in Fine-tuning Pre-trained Models For Enhanced Fairness and Efficiency, by Yixuan Zhang and Feng Zhou
Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency
by Yixuan Zhang, Feng Zhou
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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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 paper introduces an efficient and robust fine-tuning framework to mitigate biases in new tasks by neutralizing the importance of influential weights that affect predictions for different demographic groups. The framework leverages a transfer learning strategy, which uses Fisher information across demographic groups to determine the weights to modify, and integrates it with a matrix factorization technique to reduce computational demands. Experimental results demonstrate the effectiveness of this approach on multiple pre-trained models and new tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes AI fairer by stopping biases in new tasks. It gives us a way to make sure our predictions aren’t unfair towards certain groups just because they’re different. We do this by looking at which parts of the model affect its decisions for different people, and then we adjust those parts so that everyone is treated equally. This helps keep AI honest and ensures it’s not making judgments based on who someone is, rather than what they are doing. |
Keywords
* Artificial intelligence * Fine tuning * Transfer learning
