The risk of racial bias while tracking influenza-related content on social media using machine learning
The risk of racial bias while tracking influenza-related content on social media using machine learning
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DOI：
10.1093/jamia/ocaa326
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发表时间：
2021-01
期刊：
影响因子：
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通讯作者：
Brandon Lwowski;Anthony Rios
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文献类型：
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作者：
Brandon Lwowski;Anthony Rios
机器学习用于理解和跟踪社交媒体上的流感相关内容。由于这些系统被大规模使用，它们有可能对它们所帮助的人产生不利影响。在这项研究中，我们探讨了不同机器学习方法在检测流感相关内容的特定任务中的偏差。我们比较了每个模型在标准美国英语（SAE）与非裔美国英语（AAE）撰写的推文上的性能。材料与方法使用两个流感相关数据集，训练3个具有不同特征集的文本分类模型（支持向量机，卷积神经网络，双向长短期记忆）。这些数据集与SAE和AAE示例之间存在很大不平衡的真实场景相匹配。在两个数据集中，每个类别的AAE示例数量范围从2%到5%。我们还通过欠采样使用平衡数据集来评估每个模型的性能。结果我们发现，所有测试的机器学习方法在两个数据集上都有偏差。SAE和AAE示例之间的假阳性率差异范围为0.01至0.35。假阴性率的差异范围为0.01至0.23。我们还发现，神经网络方法通常有更多的不公平的结果比线性支持向量机在所选的数据集。导致最不公平预测的模型可能因数据集而异。从业人员应该意识到将机器学习应用于与健康相关的社交媒体数据的潜在危害。至少，我们建议使用传统的评估指标来评估公平性沿着。
OBJECTIVE Machine learning is used to understand and track influenza-related content on social media. Because these systems are used at scale, they have the potential to adversely impact the people they are built to help. In this study, we explore the biases of different machine learning methods for the specific task of detecting influenza-related content. We compare the performance of each model on tweets written in Standard American English (SAE) vs African American English (AAE). MATERIALS AND METHODS Two influenza-related datasets are used to train 3 text classification models (support vector machine, convolutional neural network, bidirectional long short-term memory) with different feature sets. The datasets match real-world scenarios in which there is a large imbalance between SAE and AAE examples. The number of AAE examples for each class ranges from 2% to 5% in both datasets. We also evaluate each model's performance using a balanced dataset via undersampling. RESULTS We find that all of the tested machine learning methods are biased on both datasets. The difference in false positive rates between SAE and AAE examples ranges from 0.01 to 0.35. The difference in the false negative rates ranges from 0.01 to 0.23. We also find that the neural network methods generally has more unfair results than the linear support vector machine on the chosen datasets. CONCLUSIONS The models that result in the most unfair predictions may vary from dataset to dataset. Practitioners should be aware of the potential harms related to applying machine learning to health-related social media data. At a minimum, we recommend evaluating fairness along with traditional evaluation metrics.