AINeutralarXiv – CS AI · 5h ago6/10
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Fairness of Classifiers in the Presence of Constraints between Features
Researchers propose a new fairness framework for machine learning classifiers that defines fairness through fair explanations—prime-implicant reasons for decisions that exclude protected features like gender. The study reveals that feature constraints can obscure discriminatory dependencies and that ignoring these constraints fundamentally changes fairness assessments, establishing computational complexity benchmarks for three distinct fairness definitions.
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