# Data Vision: Learning to See Through Algorithmic Abstraction

arXiv: https://arxiv.org/abs/2002.03387 · HTML: https://mlsanity.com/paper/2002.03387/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2020.

## Built on by (most cited first, 11 of 11)

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- [Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?](https://mlsanity.com/paper/2109.08131/) (2021)
- [A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI](https://mlsanity.com/paper/2302.05284/) (2023)
- [The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture Technology](https://mlsanity.com/paper/2401.10877/) (2024)
- [Towards Multifaceted Human-Centered AI](https://mlsanity.com/paper/2301.03656/) (2023)

## Abstract (arXiv)

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.