The DOI matches its Crossref record; 101 of the 101 references it cites were found in a registry or an index.
Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects
Open accessAbstract
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.
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“However, the trustworthiness of data science outcomes in practice is often a ected by tensions arising through ongoing forms of work (Passi and Jackson 2018).”
From Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes (2020) · cites this paper as Passi and Jackson 2018
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“Data governance establishes data management processes which manage data quality (Passi and Jackson 2018)”
From Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes (2020) · cites this paper as Passi and Jackson 2018
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“For example, there is a particular focus on ensuring competence of AI (i.e., that it performs accurately and eciently) [20,41], which could potentially lead to increase in end-users' condence in AI [61] (though without necessarily always implying trust [39]).”
From Being Trustworthy is Not Enough: How Untrustworthy Artificial Intelligence (AI) Can Deceive the End-Users and Gain Their Trust (2023) · cites this paper as [39]
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“Requires resource allocation for auditing”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [255]
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“Ensures reliability of data”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [255]
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Citations
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Metadata
- Authors
- Passi, S., & Jackson, S. J.
- Affiliations
- Cornell University (US)
- ORCID
- Passi, Samir
- Venue
- Proceedings of the ACM on Human-Computer Interaction
- Published
- 2018-11
- Volume
- 2
- Issue
- CSCW
- Pages
- 1-28
- ISSN
- 2573-0142 (ISSN-L)
- OpenAlex
- W4288083705
- Cited by
- 225
- References
- 100
- Citations by year
- 2026: 18 · 2025: 34 · 2024: 34 · 2023: 38 · 2022: 39 · 2021: 32 · 2020: 24 · 2019: 6
- FWCI
- 31.17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (CHS-1526155)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Systems Theories and Implementation
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- applied data science, trust negotiation, credibility, skepticism, model interpretability, data quality, uncertainty, collaboration, critical data studies, sociology of science, computer-supported cooperative work
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