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

Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects