提出可拒答的机器学习系统，探讨其哲学意义与自主解释能力。
Abstaining Machine Learning -- Philosophical Considerations
- 构建拒答型机器学习系统，按哲学中的悬置判断分类
- 其中一类更符合悬置判断标准，且能自主生成拒答并解释原因
- 适合关注模型透明性与伦理决策的研究者
本文建立机器学习（ML）与哲学中关于中立行为现象的联系。研究一类能对特定任务给出中立响应的机器学习系统，即拒答式机器学习系统，该领域尚未从哲学角度深入探讨。论文介绍并分类多种拒答系统，分析不同类型在认识论上如何对应悬置判断，涵盖悬置的本质及其规范性特征。同时建议对拒答响应的自主性与可解释性进行哲学分析。特别指出，某一类拒答系统更契合悬置判断的标准，且相比其他类型更能自主生成拒答输出，并提供拒答理由的解释。
原文摘要 · Abstract (English)
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.
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