Abstract and Keywords
This chapter provides an alternate source of intuition about fairness criteria using probabilistic directed acyclic graphical models. 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 not only for their simplicity and intuitive appeal but also for their incompatibility. Graphical models have been used to motivate and expose fairness criteria in other works, especially those which work with explicitly causal criteria for fairness. The chapter then argues that graphical models provide an invaluable source of intuition even in noncausal scenarios and reveal the weakness of Equalized Odds.
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