Paper Info
Title
Data Vision: Learning to See Through Algorithmic Abstraction.
Abstract
Learning to see through data is central to contemporary forms of algorithmic knowledge production. While often represented as a mechanical application of rules, making algorithms work with data requires a great deal of situated work. This paper examines how the often-divergent demands of mechanization and discretion manifest in data analytic learning environments. Drawing on research in CSCW and the social sciences, and ethnographic fieldwork in two data learning environments, we show how an algorithm's application is seen sometimes as a mechanical sequence of rules and at other times as an array of situated decisions. Casting data analytics as a rule-based (rather than rule-bound) practice, we show that effective data vision requires would-be analysts to straddle the competing demands of formal abstraction and empirical contingency. We conclude by discussing how the notion of data vision can help better leverage the role of human work in data analytic learning, research, and practice.
Year
DOI
Venue
2017
10.1145/2998181.2998331
CSCW
Keywords
Field
DocType
Data Vision, Data Analysis, Professional Vision, Machine Learning, Digital Humanities, Professionalization
Situated,Data science,Professionalization,Abstraction,Computer-supported cooperative work,Data analysis,Computer science,Knowledge management,Human–computer interaction,Straddle,Discretion,Contingency
Conference
ISSN
Citations 
PageRank 
In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. ACM, New York, NY, USA, 2436-2447
7
0.46
References 
Authors
9
2
Name
Order
Citations
PageRank
Samir Passi180.81
Steven J. Jackson238027.24