为质性研究打造安全可信的AI工具，突破现有通用模型局限。
Not Everything That Counts Can Be Counted: A Case for Safe Qualitative AI
- 专为质性研究设计可解释、可复现的AI系统
- 解决现有工具偏见多、不透明、隐私风险高的问题
- 适合需要深度意义解析的跨学科研究者
人工智能与大语言模型正重塑科学范式，多数进展集中于全自动量化科研流程。然而，质性研究仍被忽视：研究者虽有使用意愿，却只能依赖ChatGPT等通用工具处理访谈分析、数据标注与主题建模，而这些工具普遍存在偏见、不可解释、不可复现及隐私泄露风险。这导致一个关键缺口——尽管量化方法已高度自动化，但对意义建构与全面科学理解至关重要的质性维度仍未有效整合。本文主张从零构建专用于解释性研究的质性AI系统，强调其必须具备透明性、可复现性与隐私保护能力。通过梳理最新文献，我们展示如何通过稳健的质性能力增强现有自动化发现流程，并识别出安全质性AI在跨学科与混合方法研究中的关键机遇。
原文摘要 · Abstract (English)
Artificial intelligence (AI) and large language models (LLM) are reshaping science, with most recent advances culminating in fully-automated scientific discovery pipelines. But qualitative research has been left behind. Researchers in qualitative methods are hesitant about AI adoption. Yet when they are willing to use AI at all, they have little choice but to rely on general-purpose tools like ChatGPT to assist with interview interpretation, data annotation, and topic modeling - while simultaneously acknowledging these system's well-known limitations of being biased, opaque, irreproducible, and privacy-compromising. This creates a critical gap: while AI has substantially advanced quantitative methods, the qualitative dimensions essential for meaning-making and comprehensive scientific understanding remain poorly integrated. We argue for developing dedicated qualitative AI systems built from the ground up for interpretive research. Such systems must be transparent, reproducible, and privacy-friendly. We review recent literature to show how existing automated discovery pipelines could be enhanced by robust qualitative capabilities, and identify key opportunities where safe qualitative AI could advance multidisciplinary and mixed-methods research.
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