用主动采样与生成数据结合的方法修复模型偏见，仅用少量新数据就显著改善性别偏见。
Active Data Sampling and Generation for Bias Remediation
- 结合主动采样与反向偏见生成，动态补充训练数据。
- 在90:10性别失衡下修复偏见，准确率几乎不受影响。
- 适合需提升公平性的预训练模型微调场景。
充分的数据采样覆盖是训练可信机器学习模型的关键。然而，现实中数据因非概率采样常携带各类固有偏见，而进行充分随机采样代价过高且耗时。若未缓解这些偏见，可能引发有害或歧视性后果，阻碍预训练模型的大规模应用，并削弱其公平性与真实性预期。本文提出一种混合主动采样与数据生成策略——samplation，用于在微调阶段补偿预训练分类器的不公平预测，假设训练数据来自非概率采样方案。给定预训练分类器后，先在测试集上评估公平性指标，再生成新的标注数据池，并为微调生成一定数量的反向偏见人工样本。以视觉语义角色标注的深度模型为例，该方法仅用少量新增数据即完全修复了模拟的性别偏见（从90:10不平衡状态），且对准确率影响极小。
原文摘要 · Abstract (English)
Adequate sampling space coverage is the keystone to effectively train trustworthy Machine Learning models. Unfortunately, real data do carry several inherent risks due to the many potential biases they exhibit when gathered without a proper random sampling over the reference population, and most of the times this is way too expensive or time consuming to be a viable option. Depending on how training data have been gathered, unmitigated biases can lead to harmful or discriminatory consequences that ultimately hinders large scale applicability of pre-trained models and undermine their truthfulness or fairness expectations. In this paper, a mixed active sampling and data generation strategy -- called samplation -- is proposed as a mean to compensate during fine-tuning of a pre-trained classifer the unfair classifications it produces, assuming that the training data come from a non-probabilistic sampling schema. Given a pre-trained classifier, first a fairness metric is evaluated on a test set, then new reservoirs of labeled data are generated and finally a number of reversely-biased artificial samples are generated for the fine-tuning of the model. Using as case study Deep Models for visual semantic role labeling, the proposed method has been able to fully cure a simulated gender bias starting from a 90/10 imbalance, with only a small percentage of new data and with a minor effect on accuracy.
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