  There is a growing body of work on learning from human feedback to align
various aspects of machine learning systems with human values and preferences.
We consider the setting of fairness in content moderation, in which human
feedback is used to determine how two comments -- referencing different
sensitive attribute groups -- should be treated in comparison to one another.
With a novel dataset collected from Prolific and MTurk, we find significant
gaps in fairness preferences depending on the race, age, political stance,
educational level, and LGBTQ+ identity of annotators. We also demonstrate that
demographics mentioned in text have a strong influence on how users perceive
individual fairness in moderation. Further, we find that differences also exist
in downstream classifiers trained to predict human preferences. Finally, we
observe that an ensemble, giving equal weight to classifiers trained on
annotations from different demographics, performs better for different
demographic intersections; compared to a single classifier that gives equal
weight to each annotation.
