Gender and Racial Fairness in Depression Research using Social Media
Gender and Racial Fairness in Depression Research using Social Media
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DOI：
10.18653/v1/2021.eacl-main.256
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发表时间：
2021-03
期刊：
影响因子：
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通讯作者：
Carlos Alejandro Aguirre;Keith Harrigian;Mark Dredze
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文献类型：
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作者：
Carlos Alejandro Aguirre;Keith Harrigian;Mark Dredze
多项研究表明，在线社交媒体平台上表达的行为可以表明一个人的心理健康状态。这类数据的广泛使用激起了人们对心理健康研究的兴趣，使用了几个被贴上心理健康状况标签的数据集。虽然之前的研究已经提出了对这些数据产生的模型中可能存在的偏见的担忧，但还没有研究调查这些偏见在数据中如何在人口统计群体中表现出来，比如性别和种族/民族群体。在这里，我们分析了基于Twitter数据训练的抑郁症分类器在性别和种族人口统计群体方面的公平性。我们发现，对于代表性不足的群体，模型的表现有所不同，我们调查了这些偏差的来源，而不仅仅是数据表示。我们的研究结果就如何在未来的研究中避免这些偏见提出了建议。
Multiple studies have demonstrated that behaviors expressed on online social media platforms can indicate the mental health state of an individual. The widespread availability of such data has spurred interest in mental health research, using several datasets where individuals are labeled with mental health conditions. While previous research has raised concerns about possible biases in models produced from this data, no study has investigated how these biases manifest themselves with regards to demographic groups in data, such as gender and racial/ethnic groups. Here, we analyze the fairness of depression classifiers trained on Twitter data with respect to gender and racial demographic groups. We find that model performance differs for underrepresented groups, and we investigate sources of these biases beyond data representation. Our study results in recommendations on how to avoid these biases in future research.