论文标题
对数据科学的信任：公司数据科学项目中的协作，翻译和问责制
Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects
论文作者
论文摘要
数据科学系统在应用和现实世界中的可信度是从特定紧张局势通过位置，务实和持续的工作形式出现的。利用CSCW的研究，批判数据研究以及科学的历史和社会学以及与公司数据科学团队的六个月的沉浸式民族志实地调查，我们描述了应用数据科学工作中的四个共同紧张局势：（UN）等效数字，（反对）直觉知识，（IN）可靠数据，以及（IN）可审查模型。我们展示了组织参与者如何通过怀疑，评估和信誉的实践在混乱和不确定的分析条件下建立和重新谈判信任。强调了现实世界数据科学的协作和异质性质，我们展示了对应用的公司数据科学设置的信任管理不仅取决于预处理和量化，还取决于谈判和翻译。最后，我们通过讨论了CSCW内外的数据科学研究和实践的发现的含义。
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.
