The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
How do Data Science Workers Collaborate? Roles, Workflows, and Tools
Open accessAbstract
Today, the prominence of data science within organizations has given rise to teams of data science workers collaborating on extracting insights from data, as opposed to individual data scientists working alone. However, we still lack a deep understanding of how data science workers collaborate in practice. In this work, we conducted an online survey with 183 participants who work in various aspects of data science. We focused on their reported interactions with each other (e.g., managers with engineers) and with different tools (e.g., Jupyter Notebook). We found that data science teams are extremely collaborative and work with a variety of stakeholders and tools during the six common steps of a data science workflow (e.g., clean data and train model). We also found that the collaborative practices workers employ, such as documentation, vary according to the kinds of tools they use. Based on these findings, we discuss design implications for supporting data science team collaborations and future research directions.
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Model Cards for Model Reporting
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“Recent work in this space similarly argues for more documentation to improve transparency, as well as greater standardization around documentation [27, 65], particularly important when it comes to publicly-released datasets and models.”
BackgroundGuidelines for Human-AI Interaction
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Communities of practice: A brief introduction
Wenger, É. (2009) · Scholars' Bank (University of Oregon) · Cited by 2,349
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Coordination in software development
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Social coding in GitHub: transparency and collaboration in an open software repository
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Questioning the AI: Informing Design Practices for Explainable AI User Experiences
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AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias
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Wang, D., Churchill, E., Maes, P., Fan, X., Shneiderman, B., Shi, Y., & Wang, Q. (2020) · Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3334480.3381069 · Cited by 271
Self-citationHuman factors in model interpretability: Industry practices, challenges, and needs
Hong, S. R., Hullman, J., & Bertini, E. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3392878 · Cited by 219
How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study
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Self-citationStudying Up Machine Learning Data: Why Talk About Bias When We Mean Power?
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Metadata
- Authors
- Zhang, A. X., Muller, M., & Wang, D.
- Venue
- Proceedings of the ACM on Human-Computer Interaction
- Published
- 2020-05-28
- Volume
- 4
- Issue
- CSCW1
- Pages
- 1-23
- ISSN
- 2573-0142 (ISSN-L)
- OpenAlex
- W3029504795
- Cited by
- 268
- References
- 70
- FWCI
- 37.19
- Type
- journal article
- Language
- en
- Open access
- green
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