论文标题
使用因果模型中的变异推断来改善公平预测
Improving Fair Predictions Using Variational Inference In Causal Models
论文作者
论文摘要
算法公平的重要性随着机器学习对人们的生活的影响而增长。关于公平指标的最新工作表明，公平限制的因果推理的必要性。在这项工作中，提出了一种名为Fairtrade的实用方法，用于创建灵活的预测模型，该模型将公平性约束在敏感的因果路径上整合。该方法使用差异推理的最新进展，以说明未观察到的混杂因素。此外，提出了一个方法概述，该方法使用因果机制估计来审核黑匣子模型。在检测非法社会福利的背景下，对模拟数据和真实数据集进行了实验。这项研究旨在为纪念我们道德和法律界限的机器学习技术做出贡献。
The importance of algorithmic fairness grows with the increasing impact machine learning has on people's lives. Recent work on fairness metrics shows the need for causal reasoning in fairness constraints. In this work, a practical method named FairTrade is proposed for creating flexible prediction models which integrate fairness constraints on sensitive causal paths. The method uses recent advances in variational inference in order to account for unobserved confounders. Further, a method outline is proposed which uses the causal mechanism estimates to audit black box models. Experiments are conducted on simulated data and on a real dataset in the context of detecting unlawful social welfare. This research aims to contribute to machine learning techniques which honour our ethical and legal boundaries.
