# Supporting Serendipity: Opportunities and Challenges for Human-AI Collaboration in Qualitative Analysis

arXiv: https://arxiv.org/abs/2102.03702 · HTML: https://mlsanity.com/paper/2102.03702/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2021.

## Built on by (most cited first, 11 of 11)

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- [Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants](https://mlsanity.com/paper/2406.18675/) (2024)
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- [Optimising Human-Machine Collaboration for Efficient High-Precision Information Extraction from Text Documents](https://mlsanity.com/paper/2302.09324/) (2023)
- [To Classify is to Interpret: Building Taxonomies from Heterogeneous Data through Human-AI Collaboration](https://mlsanity.com/paper/2307.16481/) (2023)
- [Designing Computational Tools for Exploring Causal Relationships in Qualitative Data](https://mlsanity.com/paper/2602.06506/) (2026)
- [Value-Sensitive AI for Prayer: Balancing the Agencies Between Human and AI Agents in Spiritual Context](https://mlsanity.com/paper/2604.25230/) (2026)

## Abstract (arXiv)

Qualitative inductive methods are widely used in CSCW and HCI research for their ability to generatively discover deep and contextualized insights, but these inherently manual and human-resource-intensive processes are often infeasible for analyzing large corpora. Researchers have been increasingly interested in ways to apply qualitative methods to "big" data problems, hoping to achieve more generalizable results from larger amounts of data while preserving the depth and richness of qualitative methods. In this paper, we describe a study of qualitative researchers' work practices and their challenges, with an eye towards whether this is an appropriate domain for human-AI collaboration and what successful collaborations might entail. Our findings characterize participants' diverse methodological practices and nuanced collaboration dynamics, and identify areas where they might benefit from AI-based tools. While participants highlight the messiness and uncertainty of qualitative inductive analysis, they still want full agency over the process and believe that AI should not interfere. Our study provides a deep investigation of task delegability in human-AI collaboration in the context of qualitative analysis, and offers directions for the design of AI assistance that honor serendipity, human agency, and ambiguity.