Addressing Bias Through Ensemble Learning and Regularized Fine-Tuning

Understand

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.