Fairness in machine learning seeks to mitigate model bias against individuals based on sensitive features such as sex or age, often caused by an uneven representation of the population in the training data due to selection bias. Notably, bias unascribed to sensitive features is challenging to identify and typically goes undiagnosed, despite its prominence in complex high-dimensional data from fields like computer vision and molecular biomedicine. Strategies to mitigate unidentified bias and evaluate mitigation methods are crucially needed, yet remain underexplored. We introduce: (i) Diverse Class-Aware Self-Training (DCAST), model-agnostic mitigation aware of class-specific bias, which promotes sample diversity to counter confirmation bias of conventional self-training while leveraging unlabeled samples for an improved representation of the underlying population; (ii) hierarchy bias, multivariate and class-aware bias induction without prior knowledge. Models learned with DCAST showed improved robustness to hierarchy and other biases across eleven datasets, against conventional self-training and six prominent domain adaptation techniques. Advantage was largest for higher-dimensional datasets, suggesting DCAST as a promising strategy to achieve fairer learning beyond identifiable bias.

本研究针对机器学习中的选择偏差问题，该偏差通常导致模型对性别或年龄等敏感特征的偏见。提出了一种新的方法DCAST，通过增强样本多样性，利用未标记样本改善基础人口的表示，同时有效识别和缓解复杂数据中的未识别偏差，从而在多个数据集上展现出更强的鲁棒性和公平性。

多样性类识别自我训练：缓解选择偏差以实现更公正的学习