论文标题
使用音频特征检测抑郁症检测中的性别偏见
Gender Bias in Depression Detection Using Audio Features
论文作者
论文摘要
抑郁症是一个大规模的心理健康问题，也是机器学习研究人员在发现抑郁症方面的挑战领域。诸如遇险分析访谈语料库之类的数据集 - 绿野仙踪（DAIC -WOZ）是为了帮助该领域的研究。但是，除了准确检测抑郁症所固有的挑战之外，数据集中的偏见可能导致分类性能偏斜。在本文中，我们检查了DAIC-WOZ数据集中的性别偏见。我们表明，daic-woz中的性别偏见会导致表现过多。通过公平机器学习的不同概念，例如数据重新分布以及使用原始音频功能，我们可以减轻偏见的有害影响。
Depression is a large-scale mental health problem and a challenging area for machine learning researchers in detection of depression. Datasets such as Distress Analysis Interview Corpus - Wizard of Oz (DAIC-WOZ) have been created to aid research in this area. However, on top of the challenges inherent in accurately detecting depression, biases in datasets may result in skewed classification performance. In this paper we examine gender bias in the DAIC-WOZ dataset. We show that gender biases in DAIC-WOZ can lead to an overreporting of performance. By different concepts from Fair Machine Learning, such as data re-distribution, and using raw audio features, we can mitigate against the harmful effects of bias.
