
Contents
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Introduction Introduction
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Graphical Models Graphical Models
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Probabilistic Directed Acyclic Graphical Models Probabilistic Directed Acyclic Graphical Models
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Causality Causality
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Three Criteria for Fairness Three Criteria for Fairness
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Demographic Parity and Independence Demographic Parity and Independence
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Equalized Odds and Separation Equalized Odds and Separation
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Calibration by Group and Sufficiency Calibration by Group and Sufficiency
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Fairness Criteria in Two Scenarios Fairness Criteria in Two Scenarios
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Scenario 1: Loan Repayment Scenario 1: Loan Repayment
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Independence Independence
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Separation Separation
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Sufficiency Sufficiency
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Scenario 2: Job Advertisement Scenario 2: Job Advertisement
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Independence Independence
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Separation Separation
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Sufficiency Sufficiency
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Understanding Separation Understanding Separation
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Incompatibility between Separation and Sufficiency Incompatibility between Separation and Sufficiency
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Parity by Signal Parity by Signal
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Causal Considerations Causal Considerations
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Scenario 3: College Admissions Scenario 3: College Admissions
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Scenario 4: Insurance Prices Scenario 4: Insurance Prices
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Conclusion Conclusion
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Acknowledgments Acknowledgments
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Bibliography Bibliography
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26 Fairness Criteria through the Lens of Directed Acyclic Graphs: A Statistical Modeling Perspective
Get accessBenjamin R. Baer, Department of Statistics and Data Science, Cornell University
Daniel E. Gilbert, Department of Statistics and Data Science, Cornell University
Martin T. Wells, Department of Statistics and Data Science, Cornell University
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Published:09 July 2020
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Abstract
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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