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

Abstract: 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.

• The paper demonstrates that AI can augment qualitative analysis by managing vast data while preserving human interpretive control.
• It outlines a human-AI collaboration framework emphasizing transparency, flexibility, and AI as a suggestive partner rather than a decision-maker.
• Key design considerations include modular systems that integrate with existing workflows and promote serendipitous discoveries.

Supporting Serendipity in Human-AI Collaboration for Qualitative Analysis

The paper "Supporting Serendipity: Opportunities and Challenges for Human-AI Collaboration in Qualitative Analysis" explores the potential for AI to augment qualitative research methods typically employed in CSCW and HCI. The core argument is the utility of AI in handling large scale data while preserving the depth and insights characteristic of qualitative methods.

Context and Motivation

Qualitative research in CSCW and HCI involves methodologies like interviews and coding for gaining insights into user behavior and social interactions. Despite its advantages in depth and contextual understanding, traditional qualitative analysis is inherently manual and labor-intensive, presenting challenges in scaling to handle larger datasets typical of the big data era. Consequently, the paper investigates whether AI can assist in the qualitative analysis without undermining the human-centric nature of qualitative research.

Current Practices and Challenges

The paper identifies key challenges faced by qualitative researchers, such as the overwhelming volume of data and the burden of manual coding. While computational tools, including CAQDAS, offer some support, researchers often find these tools inadequate or too rigid, leading to an underutilization of advanced features. Moreover, the personal, interpretive nature of qualitative research fosters resistance to complete automation, as researchers value the serendipity and creativity involved in making novel connections within data.

Human-AI Collaboration Framework

Grounded in interviews with qualitative researchers, the paper discusses a nuanced framework for human-AI collaboration:

1. Assistance in Data Handling: AI can help manage data volume through initial sorting and identification of potential themes or outliers without mandating conclusive labels, thereby preserving human-led interpretation.
2. Providing Suggestions, Not Answers: AI systems should function as suggestive collaborators rather than deterministic tools. They could highlight data patterns or inconsistencies but leave final interpretative judgments to human researchers.
3. Transparency and Trust: To facilitate trustworthy collaboration, AI systems must be transparent in their operations and outputs to allow human users to make informed interpretations augmenting rather than overriding them.
4. Promotion of Serendipity: By allowing researchers to explore AI-suggested pathways, the collaboration should foster unexpected discoveries, aligning with qualitative research goals.

Designing AI Systems for Qualitative Research

Design considerations for AI tools in qualitative research include focusing on modular and flexible systems that respect user autonomy and the iterative nature of qualitative analysis. Moreover, these systems should integrate seamlessly with existing research workflows and support diverse datasets, including text, audio, and visual data.

Conclusion

The paper argues for a conservative and supportive role for AI in qualitative analysis—a role that supports serendipity and human agency. Instead of replacing researchers, AI can enhance their ability to draw deep insights from complex data encompasses respecting the methodological and epistemological principles that underlie qualitative research. The future of AI in this domain lies in crafting systems that complement the creative and interpretive strengths of human analysts without obviating the human touch that defines qualitative exploration.