Julia工具包FairML实现三阶段公平分类，缓解预测不公。
FairML: A Julia Package for Fair Classification
- 分预处理、训练中、后处理三阶段降低不公平性
- 通过重采样和阈值调整，有效减少偏差影响
- 兼容MLJ.jl模型，适合关注算法公平性的研究者
本文提出FairML.jl，一个用于机器学习公平分类的Julia工具包。该框架将公平学习过程分为三个阶段：预处理阶段采用重采样方法缓解数据不平衡带来的不公平；训练中阶段支持MLJ.jl中的分类器或用户自定义模型，集成公平优化机制以减轻歧视性预测；后处理阶段探讨公平预测的阈值选择。通过模拟实验，验证了各阶段及组合方案在降低歧视性影响方面的有效性。
原文摘要 · Abstract (English)
In this paper, we propose FairML.jl, a Julia package providing a framework for fair classification in machine learning. In this framework, the fair learning process is divided into three stages. Each stage aims to reduce unfairness, such as disparate impact and disparate mistreatment, in the final prediction. For the preprocessing stage, we present a resampling method that addresses unfairness coming from data imbalances. The in-processing phase consist of a classification method. This can be either one coming from the MLJ.jl package, or a user defined one. For this phase, we incorporate fair ML methods that can handle unfairness to a certain degree through their optimization process. In the post-processing, we discuss the choice of the cut-off value for fair prediction. With simulations, we show the performance of the single phases and their combinations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。