Interrogating Data Work as a Community of Practice

• Published in: Proceedings of the ACM on human-computer interaction 2022-11, Vol.6 (CSCW2), p.1-28, Article 307
• Main Authors: Rothschild, Annabel, Meng, Amanda, DiSalvo, Carl, Johnson, Britney, Shapiro, Ben Rydal, DiSalvo, Betsy
• Format: Article
• Language: English
• Subjects: Collaborative and social computing; Collaborative and social computing theory, concepts and paradigms; Computer supported cooperative work; Human-centered computing
• ISSN: 2573-0142; 2573-0142
• DOI: 10.1145/3555198

We apply Lave & Wenger's construct of a community of practice to identify and position members of the data work community of practice, focusing on members on the periphery who have received less attention - as compared to full practitioners (e.g., data scientists). Reporting on results of interviews with 19 civic workers who perform data work as their main task, we identify an atypical relationship between subject-domain experts (such as our interviewees) and full members of the data work community. Our interviewees may have less computational skill in data work, but they have extensive and varied practices to engage in data contextualization that data scientists and other full community members could learn from. In identifying the attributes of data workers on the periphery, we also hope to call attention to the challenges they face in performing data work in low resources institutions (e.g., governmental, non-profit). Our findings contribute to the larger conversations in human-centered data science about who performs data work and how they go about it, in order to addresses questions of power, fairness, and bias in data-intensive systems.