Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency

• URL: http://arxiv.org/abs/2403.00625v1
• Date: Fri, 1 Mar 2024 16:01:28 GMT
• Title: Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency
• Authors: Yixuan Zhang and Feng Zhou
• Abstract summary: We introduce an efficient and robust fine-tuning framework specifically designed to mitigate biases in new tasks. Our empirical analysis shows that the parameters in the pre-trained model that affect predictions for different demographic groups are different. We employ a transfer learning strategy that neutralizes the importance of these influential weights, determined using Fisher information across demographic groups.
• Score: 26.86557244460215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-tuning pre-trained models is a widely employed technique in numerous real-world applications. However, fine-tuning these models on new tasks can lead to unfair outcomes. This is due to the absence of generalization guarantees for fairness properties, regardless of whether the original pre-trained model was developed with fairness considerations. To tackle this issue, we introduce an efficient and robust fine-tuning framework specifically designed to mitigate biases in new tasks. Our empirical analysis shows that the parameters in the pre-trained model that affect predictions for different demographic groups are different, so based on this observation, we employ a transfer learning strategy that neutralizes the importance of these influential weights, determined using Fisher information across demographic groups. Additionally, we integrate this weight importance neutralization strategy with a matrix factorization technique, which provides a low-rank approximation of the weight matrix using fewer parameters, reducing the computational demands. Experiments on multiple pre-trained models and new tasks demonstrate the effectiveness of our method.

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