Addressing Bias Through Ensemble Learning and Regularized Fine-Tuning
- URL: http://arxiv.org/abs/2402.00910v2
- Date: Tue, 13 Feb 2024 18:54:59 GMT
- Title: Addressing Bias Through Ensemble Learning and Regularized Fine-Tuning
- Authors: Ahmed Radwan, Layan Zaafarani, Jetana Abudawood, Faisal AlZahrani,
Fares Fourati
- Abstract summary: This paper proposes a comprehensive approach using multiple methods to remove bias in AI models.
We train multiple models with the counter-bias of the pre-trained model through data splitting, local training, and regularized fine-tuning.
We conclude our solution with knowledge distillation that results in a single unbiased neural network.
- Score: 0.2812395851874055
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Addressing biases in AI models is crucial for ensuring fair and accurate
predictions. However, obtaining large, unbiased datasets for training can be
challenging. This paper proposes a comprehensive approach using multiple
methods to remove bias in AI models, with only a small dataset and a
potentially biased pretrained model. We train multiple models with the
counter-bias of the pre-trained model through data splitting, local training,
and regularized fine-tuning, gaining potentially counter-biased models. Then,
we employ ensemble learning for all models to reach unbiased predictions. To
further accelerate the inference time of our ensemble model, we conclude our
solution with knowledge distillation that results in a single unbiased neural
network. We demonstrate the effectiveness of our approach through experiments
on the CIFAR10 and HAM10000 datasets, showcasing promising results. This work
contributes to the ongoing effort to create more unbiased and reliable AI
models, even with limited data availability.
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