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

TL;DR

This paper explores how trust is built and managed in real-world corporate data science projects through practices of skepticism, assessment, and negotiation amid uncertain and complex analytic conditions.

Contribution

It provides an ethnographic analysis of trust dynamics in applied data science, highlighting the importance of negotiation and translation in establishing credibility.

Findings

Trust involves skepticism, assessment, and credibility practices.

Organizational trust depends on negotiation and translation.

Managing trust requires handling messy, uncertain data environments.

Abstract

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.