用多样性引导的自训练缓解数据偏差，提升模型公平性。
Metric-DST: Mitigating Selection Bias Through Diversity-Guided Semi-Supervised Metric Learning
- 通过度量学习空间选择多样样本，避免仅选高置信度数据
- 在合成与真实数据集上均显著降低偏差，提升模型鲁棒性
- 适用于有偏差的数据场景，尤其适合追求公平性的研究者
选择偏差是机器学习公平性中的关键挑战，因训练数据对人群代表性不足，可能导致对少数群体表现不佳。半监督学习如自训练可通过引入无标签数据来理解整体分布，缓解偏差。然而传统自训练仅选择高置信度样本，可能强化已有偏差。本文提出Metric-DST，一种基于多样性的自训练策略，利用度量学习的隐式嵌入空间，主动纳入更多样化的样本以对抗信心驱动的偏差。该方法在具有人为引入偏差的生成数据和真实世界数据集，以及存在固有偏差的分子生物学预测任务中，均学习到更稳健的模型。Metric-DST提供了一种灵活、普适的解决方案，可有效缓解选择偏差，提升模型公平性。
原文摘要 · Abstract (English)
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.
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