  The trustworthiness of data science systems in applied and real-world
settings emerges from the resolution of specific tensions through situated,
pragmatic, and ongoing forms of work. Drawing on research in CSCW, critical
data studies, and history and sociology of science, and six months of immersive
ethnographic fieldwork with a corporate data science team, we describe four
common tensions in applied data science work: (un)equivocal numbers,
(counter)intuitive knowledge, (in)credible data, and (in)scrutable models. We
show how organizational actors establish and re-negotiate trust under messy and
uncertain analytic conditions through practices of skepticism, assessment, and
credibility. Highlighting the collaborative and heterogeneous nature of
real-world data science, we show how the management of trust in applied
corporate data science settings depends not only on pre-processing and
quantification, but also on negotiation and translation. We conclude by
discussing the implications of our findings for data science research and
practice, both within and beyond CSCW.
