论文标题
迈向基于类比的机器学习解释
Towards Analogy-Based Explanations in Machine Learning
论文作者
论文摘要
最近在机器学习的背景下应用了类似推理的原则，例如开发分类和偏好学习的新方法。在本文中，我们认为，尽管类似推理对于以高预测精度构建新的学习算法肯定是有用的，但从可解释性和解释性的角度来看，可以说并不是有趣的。更具体地说，我们认为基于类比的方法是可解释的AI和可解释的机器学习领域现有方法的可行替代方法，并且基于类比的解释机器学习算法可以以有意义的方式补充基于相似性的解释。为了证实这些主张，我们概述了基于类比的解释的基本思想，并通过某些示例说明了其潜在有用性。
Principles of analogical reasoning have recently been applied in the context of machine learning, for example to develop new methods for classification and preference learning. In this paper, we argue that, while analogical reasoning is certainly useful for constructing new learning algorithms with high predictive accuracy, is is arguably not less interesting from an interpretability and explainability point of view. More specifically, we take the view that an analogy-based approach is a viable alternative to existing approaches in the realm of explainable AI and interpretable machine learning, and that analogy-based explanations of the predictions produced by a machine learning algorithm can complement similarity-based explanations in a meaningful way. To corroborate these claims, we outline the basic idea of an analogy-based explanation and illustrate its potential usefulness by means of some examples.
