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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 gainedsignificant attention for their simplicity and intuitive appeal, but also fortheir incompatibility. This chapter provides a perspective on the meaning andconsequences of these and other fairness criteria using graphical models whichreveals Equalized Odds and related criteria to be ultimately misleading. Anassessment of various graphical models suggests that fairness criteria shouldultimately be case-specific and sensitive to the nature of the information thealgorithm processes.

Fairness criteria through the lens of directed acyclic graphical models