Selection bias poses a critical challenge for fairness in machine learning, as models trained on data that is less representative of the population might exhibit undesirable behavior for underrepresented profiles. Semi-supervised learning strategies like self-training can mitigate selection bias by incorporating unlabeled data into model training to gain further insight into the distribution of the population. However, conventional self-training seeks to include high-confidence data samples, which may reinforce existing model bias and compromise effectiveness. We propose Metric-DST, a diversity-guided self-training strategy that leverages metric learning and its implicit embedding space to counter confidence-based bias through the inclusion of more diverse samples. Metric-DST learned more robust models in the presence of selection bias for generated and real-world datasets with induced bias, as well as a molecular biology prediction task with intrinsic bias. The Metric-DST learning strategy offers a flexible and widely applicable solution to mitigate selection bias and enhance fairness of machine learning models.

该研究解决了机器学习中的选择偏差问题，尤其是在训练数据未能代表整体人群时所带来的不公正表现。作者提出了一种新颖的多样性指导自我训练策略Metric-DST，通过度量学习引入更多样本，以减少基于信心的偏见，从而提高模型的鲁棒性。研究发现，Metric-DST在存在选择偏差的情况下，能够有效提升生成数据和实际数据集的模型性能，增强机器学习模型的公平性。

Metric-DST：通过多样性指导的半监督度量学习来减轻选择偏差