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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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
Experimental — accuracy not yet measuredIn the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Discovery of Grounded Theory; Strategies for Qualitative Research
Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968) · Nursing Research · doi:10.1097/00006199-196807000-00014 · Cited by 39,449
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
Wallace, A. F. C., & Garfinkel, H. (1968) · American Sociological Review · doi:10.2307/2092245 · Cited by 12,413
Why Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020124 · Cited by 10,505
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682
Lipton, Z. C. (2018) · Queue · doi:10.1145/3236386.3241340 · Cited by 5,427
Why Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · CHANCE · doi:10.1080/09332480.2005.10722754 · Cited by 4,702 · cited 1×
“Several data correlations established in large datasets are neither reproducible nor falsifiable [45,61].”
BackgroundThe ethics of algorithms: Mapping the debate
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · Big Data & Society · doi:10.1177/2053951716679679 · Cited by 4,567 · cited 1×
“The increased management of data science systems is a priority [65,96,105], but “the complex decision-making structure” needed to manage them often exceeds “the human and organizational resources available for oversight” [68].”
BackgroundBusfield, J., & Garfinkel, H. (1968) · British Journal of Sociology · doi:10.2307/588840 · Cited by 3,828
The Audit Society: Rituals of Verification
Quam, L. (1998) · BMJ · doi:10.1136/bmj.316.7133.787a · Cited by 3,611
Research works printed but not linked: 19
- Geoffrey C. Bowker. 2014. The Theory/Data Thing. Int. J. Commun.8, 1795--1799.
- Kathy Charmaz. 2014. Constructing Grounded Theory (Introducing Qualitative Methods series) 2nd Edition. Sage, London.
- Harry M. Collins and Robert Evans. 2007. Rethinking Expertise. University of Chicago Press, Chicago.
- 10.1525/rep.1992.40.1.99p0137h · DOI · Paper page
- Lorraine Daston and Peter Galison. 2007. Objectivity. MIT Press Cambridge Massachusetts.
- John Dewey. 1939. Theory of Valuation. University of Chicago Press, Chicago.
- Tarleton Gillespie. 2014. The Relevance of Algorithms. In Media Technologies: Essays on Communication, Materiality, and Society, Tarleton Gillespie, Pablo J. Boczkowski and Kirsten A. Foot (eds.). MIT Press, Cambridge, 167--194.
- Lisa Gitelman. 2006. Raw Data is an Oxymoron. MIT Press, MA.
- Ian Hacking. 1990. The Taming of Chance. Cambridge University Press, Cambridge.
- Donna Haraway. 2007. Modest_Witness@Second_Millenium. Routledge London.
- Edmund Husserl. 1970. The Crisis of European Sciences and Transcendental Philosophy. Northwestern University Press, Evanston.
- Bruno Latour. 1987. Science in Action. Harvard University Press, Cambridge, MA.
- Zachary Chase Lipton. 2016. The Mythos of Model Interpretability. In2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016).
- 10.5555/3200092 · DOI
- 10.5555/2717112 · DOI
- Rita Raley. 2013. Dataveillance and Counterveillance. In"Raw Data" Is an Oxymoron, Lisa Gitelman (ed.). MIT Press, Cambridge, Massachusetts, 121--145.
- Steven Shapin. 1989. The Invisible Technician. Am. Sci.77, 6, 554--563.
- Anselm Strauss and Juliet M. Corbin. 1990. Basics of Qualitative Research: Grounded Theory Techniques and Procedures. Sage, New York.
- Alex Tabarrok. 2015. The Rise of Opaque Intelligence. Marginal Revolution. Retrieved February 15, 2018 from http://marginalrevolution.com/marginalrevolution/2015/02/opaque-intelligence.html
Cited by
Sorted by citations
“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI
Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., & Aroyo, L. M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445518 · Cited by 781
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
Wang, D., Weisz, J. D., Muller, M., Ram, P., Geyer, W., Dugan, C., Tausczik, Y., Samulowitz, H., & Gray, A. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359313 · Cited by 342
How do Data Science Workers Collaborate? Roles, Workflows, and Tools
Zhang, A. X., Muller, M. J., & Wang, D. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3392826 · Cited by 304
The Fallacy of AI Functionality
Raji, I. D., Kumar, I. E., Horowitz, A., & Selbst, A. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533158 · Cited by 251
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction
Shelby, R., Rismani, S., Henne, K., Moon, A., Rostamzadeh, N., Nicholas, P., Yilla-Akbari, N., Gallegos, J., Smart, A., Garcia, E., et al. (2023) · Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3600211.3604673 · Cited by 279
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
