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.

通过多种方法解决 AI 模型中的偏见问题，使用较小的数据集和可能存在偏见的预训练模型，通过数据分割、局部训练和正则化微调训练多个模型，然后使用集成学习获得不带偏见的预测结果，最后通过知识蒸馏形成单一不带偏见的神经网络模型，实验证明了我们方法的有效性。

通过集成学习和正则化微调解决偏差问题

Addressing Bias Through Ensemble Learning and Regularized Fine-Tuning

Prompt-based probing has been widely used in evaluating the abilities of
pretrained language models (PLMs). Unfortunately, recent studies have
discovered such an evaluation may be inaccurate, inconsistent and unreliable.
Furthermore, the lack of understanding its inner workings, combined with its
wide applicability, has the potential to lead to unforeseen risks for
evaluating and applying PLMs in real-world applications. To discover,
understand and quantify the risks, this paper investigates the prompt-based
probing from a causal view, highlights three critical biases which could induce
biased results and conclusions, and proposes to conduct debiasing via causal
intervention. This paper provides valuable insights for the design of unbiased
datasets, better probing frameworks and more reliable evaluations of pretrained
language models. Furthermore, our conclusions also echo that we need to rethink
the criteria for identifying better pretrained language models. We openly
released the source code and data at this https URL.

本文探讨了基于问题提示的探测方法可能存在的偏见、不一致性和不可靠性，强调了通过因果干预来消除偏差的必要性，并提出了更好的数据集设计、探测框架和更可靠的预训练语言模型评估标准。