Not Everything That Counts Can Be Counted: A Case for Safe Qualitative AI

📝 Original Info

• Title: Not Everything That Counts Can Be Counted: A Case for Safe Qualitative AI
• ArXiv ID: 2511.09325
• Date: 2025-11-12
• Authors: ** 논문에 명시된 저자 정보가 제공되지 않았습니다. (가능하면 원문에서 확인 필요) **

📝 Abstract

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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