2019
AAAI
AAAI 2019
Path-Specific Counterfactual Fairness
Abstract
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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Conference Pioneer
— AAAI 2019
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning
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Keyword Pioneer
— fair decision system
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Causal Inference
Machine Learning > Application Areas > Fairness
Deep Learning > Models > Variational Inference
Artificial Intelligence > Core AI > Fairness
Machine Learning > Learning Types > Deep Learning
Deep Learning > Learning Types > Self-Supervised Learning
Machine Learning > Learning Types > Causal Inference