教学生如何定义和衡量NLP中的抽象概念，避免盲目使用术语。
Teaching and Critiquing Conceptualization and Operationalization in NLP
- 通过跨学科阅读与讨论，引导学生反思'可解释性'等术语的含义。
- 强调概念定义对数据集构建、指标设计和系统评估的决定性影响。
- 适合关注AI伦理、方法论严谨性的研究者与教学者。
NLP研究者常使用'可解释性'、'偏见'、'推理'、'刻板印象'等抽象概念，却未明确定义。各子领域对这些术语有共享的理解，这种共识是制定操作决策的基础：数据集据此构建，度量指标被提出，系统性能也由此宣称。但这些概念究竟意味着什么？应当如何定义？又该如何衡量？本文介绍了一门面向学生的研讨课，旨在探讨概念化与操作化的深层问题，包含跨学科阅读材料，并注重讨论与批判性思维。
原文摘要 · Abstract (English)
NLP researchers regularly invoke abstract concepts like "interpretability," "bias," "reasoning," and "stereotypes," without defining them. Each subfield has a shared understanding or conceptualization of what these terms mean and how we should treat them, and this shared understanding is the basis on which operational decisions are made: Datasets are built to evaluate these concepts, metrics are proposed to quantify them, and claims are made about systems. But what do they mean, what should they mean, and how should we measure them? I outline a seminar I created for students to explore these questions of conceptualization and operationalization, with an interdisciplinary reading list and an emphasis on discussion and critique.
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