Missing diversity, equity, and inclusion elements in affective computing datasets directly affect the accuracy and fairness of emotion recognition algorithms across different groups. A literature review reveals how affective computing systems may work differently for different groups due to, for instance, mental health conditions impacting facial expressions and speech or age-related changes in facial appearance and health. Our work analyzes existing affective computing datasets and highlights a disconcerting lack of diversity in current affective computing datasets regarding race, sex/gender, age, and (mental) health representation. By emphasizing the need for more inclusive sampling strategies and standardized documentation of demographic factors in datasets, this paper provides recommendations and calls for greater attention to inclusivity and consideration of societal consequences in affective computing research to promote ethical and accurate outcomes in this emerging field.

翻译：情感计算数据集中多样性、公平性与包容性要素的缺失，直接影响了不同群体间情绪识别算法的准确性与公平性。文献综述揭示了情感计算系统可能因精神健康状况影响面部表情与语音、或年龄相关面部特征与健康变化等差异，而对不同群体产生差异化表现。本研究通过分析现有情感计算数据集，指出当前数据集中种族、性别/社会性别、年龄及（精神）健康状况表征方面存在令人担忧的多样性匮乏问题。本文强调需在数据集中采用更具包容性的采样策略并标准化人口统计因素文档记录，据此提出建议并呼吁情感计算研究领域加大对包容性的关注及对社会影响的考量，以推动这一新兴领域实现合乎伦理且精确的研究成果。