Articlegreen2019

Path-Specific Counterfactual Fairness

246 citations0/0 files storedAAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI))

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Ethics and Social Impacts of AI Privacy-Preserving Technologies in Data Explainable Artificial Intelligence (XAI)

Abstract

We consider the problem of learning fair decision systems from data in which a sensitive attribute might affect the decision along both fair and unfair pathways. We introduce a counterfactual approach to disregard effects along unfair pathways that does not incur in the same loss of individual-specific information as previous approaches. Our method corrects observations adversely affected by the sensitive attribute, and uses these to form a decision. We leverage recent developments in deep learning and approximate inference to develop a VAE-type method that is widely applicable to complex nonlinear models.

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2017Counterfactual Fairness 2017Avoiding Discrimination through Causal Reasoning

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2022A clarification of the nuances in the fairness metrics landscape 2023AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications 2019Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality 2024Fairness issues, current approaches, and challenges in machine learning models 2022Learning Fair Node Representations with Graph Counterfactual Fairness 2024A causal perspective on dataset bias in machine learning for medical imaging 2019Achieving Causal Fairness through Generative Adversarial Networks 2019Counterfactual Fairness: Unidentification, Bound and Algorithm

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