Title: Abstaining Machine Learning -- Philosophical Considerations Show Chinese title

Title: abstaining 机器学习 —— 哲学思考

Abstract: This paper establishes a connection between the fields of machine learning (ML) and philosophy concerning the phenomenon of behaving neutrally. It investigates a specific class of ML systems capable of delivering a neutral response to a given task, referred to as abstaining machine learning systems, that has not yet been studied from a philosophical perspective. The paper introduces and explains various abstaining machine learning systems, and categorizes them into distinct types. An examination is conducted on how abstention in the different machine learning system types aligns with the epistemological counterpart of suspended judgment, addressing both the nature of suspension and its normative profile. Additionally, a philosophical analysis is suggested on the autonomy and explainability of the abstaining response. It is argued, specifically, that one of the distinguished types of abstaining systems is preferable as it aligns more closely with our criteria for suspended judgment. Moreover, it is better equipped to autonomously generate abstaining outputs and offer explanations for abstaining outputs when compared to the other type. Show Chinese abstract

Abstract: 本文探讨了机器学习（ML）与哲学领域之间关于表现中立现象的联系。研究了一类特定的机器学习系统，它们能够针对给定任务提供中立响应，被称为回避型机器学习系统，而这一主题尚未从哲学角度进行过研究。文章介绍了多种回避型机器学习系统，并将其分类为不同的类型。研究了不同类型回避型机器学习系统的回避行为如何与认识论中的悬置判断相对应，同时讨论了悬置的本质及其规范性特征。此外，还提出了对回避响应的自主性和可解释性的哲学分析。具体而言，论证了其中一种区分明显的回避系统类型更为优选，因为它更符合我们对悬置判断的标准，而且在自主生成回避输出以及解释回避输出方面优于另一种类型。