  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.
