论文标题
多线性模型中有原则性的反事实
Principled Diverse Counterfactuals in Multilinear Models
论文作者
论文摘要
机器学习（ML）应用程序已自动化了许多现实生活任务，从而改善了私人和公共生活。但是，许多最先进的模型的黑框性质构成了模型验证的挑战。如何确定该算法基于适当的标准或不歧视某些少数群体的决定？在本文中，我们提出了一种从多线性模型中产生多种反事实解释的方法，该模型包括随机森林以及贝叶斯网络。
Machine learning (ML) applications have automated numerous real-life tasks, improving both private and public life. However, the black-box nature of many state-of-the-art models poses the challenge of model verification; how can one be sure that the algorithm bases its decisions on the proper criteria, or that it does not discriminate against certain minority groups? In this paper we propose a way to generate diverse counterfactual explanations from multilinear models, a broad class which includes Random Forests, as well as Bayesian Networks.
