厘清机器学习中假设的定义与处理方式，提升实践者认知与协作效率。
Talking About the Assumption in the Room
- 用非形式逻辑中的论证框架解析假设的构成与作用
- 22位从业者访谈揭示假设独立构建、被动应对、记录模糊三大痛点
- 为ML实践者提供系统化思考与管理假设的方法建议
在人机交互与负责任机器学习领域，关于从业者如何使用或与机器学习（ML）系统互动时所依赖的假设，已有广泛讨论。然而，现有研究尚未明确假设的概念内涵，也未说明从业者在工作流程中如何识别和处理假设，导致对何为假设及其应如何处置存在混淆。本文引入非形式逻辑中‘论证’的概念，为理解假设相关困惑提供新视角。通过对22名机器学习从业者进行半结构化访谈，发现造成混淆的核心原因在于：假设的独立构建、反应式而非反思性的处理方式，以及记录过程的模糊性。本研究将机器学习中关于假设的边缘讨论推至中心位置，并提出具体建议，帮助从业者更有效地思考和处理假设。
原文摘要 · Abstract (English)
The reference to assumptions in how practitioners use or interact with machine learning (ML) systems is ubiquitous in HCI and responsible ML discourse. However, what remains unclear from prior works is the conceptualization of assumptions and how practitioners identify and handle assumptions throughout their workflows. This leads to confusion about what assumptions are and what needs to be done with them. We use the concept of an argument from Informal Logic, a branch of Philosophy, to offer a new perspective to understand and explicate the confusions surrounding assumptions. Through semi-structured interviews with 22 ML practitioners, we find what contributes most to these confusions is how independently assumptions are constructed, how reactively and reflectively they are handled, and how nebulously they are recorded. Our study brings the peripheral discussion of assumptions in ML to the center and presents recommendations for practitioners to better think about and work with assumptions.
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