Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns

10 June 2019

Papers citing "Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns"

8 / 8 papers shown

Title
Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements Maryam Molamohammadi Afaf Taik Nicolas Le Roux G. Farnadi 42 1 0 11 Jun 2023
AI-enabled exploration of Instagram profiles predicts soft skills and personality traits to empower hiring decisions M. Harirchian F. Amin Saeed Rouhani Aref Aligholipour Vahid Amiri Lord 29 3 0 14 Dec 2022
Certifying Fairness of Probabilistic Circuits Nikil Selvam Guy Van den Broeck YooJung Choi FaML TPM 15 6 0 05 Dec 2022
FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms for Neural Networks Kiarash Mohammadi Aishwarya Sivaraman G. Farnadi 30 5 0 01 Jun 2022
A survey on datasets for fairness-aware machine learning Tai Le Quy Arjun Roy Vasileios Iosifidis Wenbin Zhang Eirini Ntoutsi FaML 11 241 0 01 Oct 2021
A fuzzy-rough uncertainty measure to discover bias encoded explicitly or implicitly in features of structured pattern classification datasets Gonzalo Nápoles Lisa Koutsoviti Koumeri 31 17 0 20 Aug 2021
Counterexample-Guided Learning of Monotonic Neural Networks Aishwarya Sivaraman G. Farnadi T. Millstein Guy Van den Broeck 24 50 0 16 Jun 2020
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,092 0 24 Oct 2016