FairCORELS, an Open-Source Library for Learning Fair Rule Lists

被引：6

作者：

Aivodji, Ulrich ^{[1
]}

Ferry, Julien ^{[2
]}

Gambs, Sebastien ^{[1
]}

Huguet, Marie-Jose ^{[2
]}

Siala, Mohamed ^{[2
]}

机构：

[1] Univ Quebec Montreal, Montreal, PQ, Canada

[2] Univ Toulouse, LAAS CNRS, Toulouse, France

来源：

PROCEEDINGS OF THE 30TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, CIKM 2021 | 2021年

关键词：

Machine Learning; Fairness; Interpretability;

D O I：

10.1145/3459637.3481965

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

FairCORELS is an open-source Python module for building fair rule lists. It is a multi-objective variant of CORELS, a branch-and-bound algorithm to learn certifiably optimal rule lists. FairCORELS supports six statistical fairness metrics, proposes several exploration parameters and leverages on the fairness constraints to prune the search space efficiently. It can easily generate sets of accuracyfairness trade-offs. The models learnt are interpretable by design and a sparsity parameter can be used to control their length.

引用

页码：4665 / 4669

页数：5

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