Graphical models show Equalized Odds and related fairness criteria to be misleading, so fairness assessments should be case-specific and depend on the algorithm's information flow.
The case for process fairness in learning: Feature selection for fair decision making
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Fairness criteria through the lens of directed acyclic graphical models
Graphical models show Equalized Odds and related fairness criteria to be misleading, so fairness assessments should be case-specific and depend on the algorithm's information flow.