  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.
