A classifier decision is fair if it has a fair explanation (prime implicant without protected features, respecting constraints); the paper relates three such fairness notions for classifiers and studies the complexity of testing them.
Explaining decisions in ML models: A parameterized complexity analy- sis
2 Pith papers cite this work. Polarity classification is still indexing.
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ASPEn integrates ASP stable-model semantics with energy-based models for joint discrete-continuous optimisation and end-to-end training on visual reasoning and multi-object tracking.
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Fairness of Classifiers in the Presence of Constraints between Features
A classifier decision is fair if it has a fair explanation (prime implicant without protected features, respecting constraints); the paper relates three such fairness notions for classifiers and studies the complexity of testing them.
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Answer Set Programming Energised! End-to-End Neurosymbolic Reasoning and Learning with ASP and Energy Based Models
ASPEn integrates ASP stable-model semantics with energy-based models for joint discrete-continuous optimisation and end-to-end training on visual reasoning and multi-object tracking.