We provide a psychometric-grounded exposition of bias and fairness as applied to a typical machine learning (ML) pipeline for affective computing (AC). 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. The various methods and metrics for measuring fairness and bias are discussed, along with pertinent implications within the U.S. 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 AC 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.

中文翻译：

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整合心理测量学和计算视角对情感计算中的偏见和公平：自动视频访谈的案例研究

我们提供了基于心理测量的偏见和公平性说明，适用于情感计算 (AC) 的典型机器学习 (ML) 管道。我们扩展了人际沟通框架，以阐明如何识别在从观察到的行为推断人类情绪和其他心理结构的过程中可能出现的偏见来源。讨论了衡量公平性和偏见的各种方法和指标，以及在美国法律背景下的相关影响。我们在一个案例研究中说明了如何衡量某些类型的偏见和公平，该案例研究涉及从模拟工作申请的视频面试中收集的多模态数据中自动推断个性和可雇用性。