抑郁预测中的多模态性别公平性：来自美国与中国数据的见解
Multimodal Gender Fairness in Depression Prediction: Insights on Data from the USA & China
AI总结：
本文首次在美国和中国两个数据集上评估抑郁预测中的多模态性别公平性，通过统计与多算法实验发现数据集差异来源尚不明确，并呼吁建立更具文化一致性的数据收集流程以减少机器学习偏见。
摘要：
社交智能体和机器人正越来越多地被用于健康福祉场景。然而，一个关键挑战在于，这些智能体和机器人通常依赖机器学习算法来检测和分析个体的心理健康状况。机器学习算法中的偏见与公平性问题正日益成为更大的关注来源。与此同时，现有文献也表明，心理健康状况在不同性别和文化之间的表现可能有所不同。我们假设，来自不同文化和性别的受试者，其特征表示（声学、文本和视觉）及其跨模态关系会存在差异，从而影响各种机器学习模型的性能和公平性。我们首次对抑郁表现中的多模态性别公平性进行了评估，在美国和中国的两个不同数据集上开展研究。我们进行了全面的统计和机器学习实验，并针对多种不同算法重复实验，以确保结果不依赖于特定算法。我们的研究结果表明，尽管两个数据集之间存在差异，但尚不能确定这种差异是由于所假设的抑郁表现差异，还是由于其他外部因素（如数据收集方法的差异）所致。我们的发现进一步呼吁建立更一致且具有文化意识的数据收集流程，以解决抑郁检测中的机器学习偏见问题，并促进开发更公平的健康福祉智能体和机器人。
英文摘要：
Social agents and robots are increasingly being used in wellbeing settings. However, a key challenge is that these agents and robots typically rely on machine learning (ML) algorithms to detect and analyse an individual's mental wellbeing. The problem of bias and fairness in ML algorithms is becoming an increasingly greater source of concern. In concurrence, existing literature has also indicated that mental health conditions can manifest differently across genders and cultures. We hypothesise that the representation of features (acoustic, textual, and visual) and their inter-modal relations would vary among subjects from different cultures and genders, thus impacting the performance and fairness of various ML models. We present the very first evaluation of multimodal gender fairness in depression manifestation by undertaking a study on two different datasets from the USA and China. We undertake thorough statistical and ML experimentation and repeat the experiments for several different algorithms to ensure that the results are not algorithm-dependent. Our findings indicate that though there are differences between both datasets, it is not conclusive whether this is due to the difference in depression manifestation as hypothesised or other external factors such as differences in data collection methodology. Our findings further motivate a call for a more consistent and culturally aware data collection process in order to address the problem of ML bias in depression detection and to promote the development of fairer agents and robots for wellbeing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
