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

TL;DR

Not Everything That Counts Can Be Counted argues that qualitative research is under-supported in AI-assisted science, with researchers often relying on general-purpose tools lacking transparency, reproducibility, and privacy. By reviewing current qualitative AI developments, the authors highlight ethical tensions, structural inequalities, and the dangers of substituting model outputs for human participants, then propose design principles—contextual and temporal awareness, human-in-the-loop operation, non-reductive reasoning, transparency, reproducibility, and local data processing—to create safe, interpretable qualitative AI. They advocate aligning with concepts like Scientist AI to build explanatory models with uncertainty quantification, enabling richer cross-method integration and multidisciplinary collaboration. The work outlines a concrete path to embed qualitative meaning-making within AI-enabled pipelines, aiming to preserve epistemic diversity while improving methodological rigor and ethical accountability in scientific practice.

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.