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• W2788578887 abstract "We consider the problem of learning fair decision systems in complex scenarios in which a sensitive attribute might affect the decision along both fair and unfair pathways. We introduce a causal approach to disregard effects along unfair pathways that simplifies and generalizes previous literature. Our method corrects observations adversely affected by the sensitive attribute, and uses these to form a decision. This avoids disregarding fair information, and does not require an often intractable computation of the path-specific effect. We leverage recent developments in deep learning and approximate inference to achieve a solution that is widely applicable to complex, non-linear scenarios." @default.
• W2788578887 created "2018-03-06" @default.
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• W2788578887 date "2018-02-22" @default.
• W2788578887 modified "2023-09-27" @default.
• W2788578887 title "Path-Specific Counterfactual Fairness." @default.
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