  A substantial portion of the literature on fairness in algorithms proposes,
analyzes, and operationalizes simple formulaic criteria for assessing fairness.
Two of these criteria, Equalized Odds and Calibration by Group, have gained
significant attention for their simplicity and intuitive appeal, but also for
their incompatibility. This chapter provides a perspective on the meaning and
consequences of these and other fairness criteria using graphical models which
reveals Equalized Odds and related criteria to be ultimately misleading. An
assessment of various graphical models suggests that fairness criteria should
ultimately be case-specific and sensitive to the nature of the information the
algorithm processes.
