We provide a psychometric-grounded exposition of bias and fairness as applied to a typical machine learning pipeline for affective computing. We expand on an interpersonal communication framework to elucidate how to identify sources of bias that may arise in the process of inferring human emotions and other psychological constructs from observed behavior. Various methods and metrics for measuring fairness and bias are discussed along with pertinent implications within the United States legal context. We illustrate how to measure some types of bias and fairness in a case study involving automatic personality and hireability inference from multimodal data collected in video interviews for mock job applications. We encourage affective computing researchers and practitioners to encapsulate bias and fairness in their research processes and products and to consider their role, agency, and responsibility in promoting equitable and just systems.

翻译：我们基于心理测量学视角，系统阐释了情感计算典型机器学习流程中的偏差与公平性问题。通过拓展人际沟通框架，阐明了如何识别从观察行为推断人类情感及其他心理构念过程中可能出现的偏差来源。讨论了多种公平性与偏差的量化方法与指标，并结合美国法律背景分析了相关影响。本研究以模拟求职场景的视频面试多模态数据为例，通过自动人格与雇用适宜性推断的案例研究，演示了如何对特定类型的偏差与公平性进行测量。我们鼓励情感计算领域的研究者与实践者将偏差与公平性纳入研究过程与产品中，思考自身在推动公平公正系统建设中的角色、能动性与责任。