FairCORELS, an Open-Source Library for Learning Fair Rule Lists
Author:
Affiliation:
1. UQAM, Montréal, Canada
2. LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France
3. LAAS-CNRS, Université de Toulouse, CNRS, INSA, Toulouse, France
Funder
Natural Sciences and Engineering Research Council of Canada
Canada Research Chairs
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3459637.3481965
Reference23 articles.
1. Learning Certifiably Optimal Rule Lists
2. Learning certifiably optimal rule lists for categorical data;Angelino Elaine;Journal of Machine Learning Research,2018
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2. Investigating Debiasing Effects on Classification and Explainability;Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society;2022-07-26
3. Improving fairness generalization through a sample-robust optimization method;Machine Learning;2022-07-06
4. Leveraging Integer Linear Programming to Learn Optimal Fair Rule Lists;Integration of Constraint Programming, Artificial Intelligence, and Operations Research;2022
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