Towards affective computing that works for everyone

arXiv (Cornell University)

Abstract

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.

Authors 2

1. Tessa Verhoef Aachen

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science Leiden University Leiden , The Netherlands

2. Eduard Fosch‐Villaronga Aachen Center for Law and Digital Technologies Leiden University Leiden

   Leiden University

   Affiliation as printed

   Center for Law and Digital Technologies Leiden University Leiden , The Netherlands