"From What I see, this makes sense": Seeing meaning in algorithmic results

TL;DR

This paper explores how meaning is constructed in data analytics through an iterative, sociotechnical process involving data, code, assumptions, and prior knowledge, emphasizing the complexity of interpreting algorithmic results.

Contribution

It highlights the situated and reflexive nature of making sense of algorithmic outputs in real-world data analytic environments, emphasizing sociotechnical aspects.

Findings

Meaning in data analytics is constructed through iterative dialogue.

Algorithmic results are sociotechnical accomplishments.

Human and technical work in data analysis are deeply intertwined.

Abstract

In this workshop paper, we use an empirical example from our ongoing fieldwork, to showcase the complexity and situatedness of the process of making sense of algorithmic results; i.e. how to evaluate, validate, and contextualize algorithmic outputs. So far, in our research work, we have focused on such sense-making processes in data analytic learning environments such as classrooms and training workshops. Multiple moments in our fieldwork suggest that meaning, in data analytics, is constructed through an iterative and reflexive dialogue between data, code, assumptions, prior knowledge, and algorithmic results. A data analytic result is nothing short of a sociotechnical accomplishment - one in which it is extremely difficult, if not at times impossible, to clearly distinguish between 'human' and 'technical' forms of data analytic work. We conclude this paper with a set of questions that we would like to explore further in this workshop.