  Algorithmic fairness research has traditionally been linked to the
disciplines of philosophy, ethics, and economics, where notions of fairness are
prescriptive and seek objectivity. Increasingly, however, scholars are turning
to the study of what different people perceive to be fair, and how these
perceptions can or should help to shape the design of machine learning,
particularly in the policy realm. The present work experimentally explores five
novel research questions at the intersection of the "Who," "What," and "How" of
fairness perceptions. Specifically, we present the results of a multi-factor
conjoint analysis study that quantifies the effects of the specific context in
which a question is asked, the framing of the given question, and who is
answering it. Our results broadly suggest that the "Who" and "What," at least,
matter in ways that are 1) not easily explained by any one theoretical
perspective, 2) have critical implications for how perceptions of fairness
should be measured and/or integrated into algorithmic decision-making systems.
