"From What I see, this makes sense": Seeing meaning in algorithmic results
This addresses the challenge of understanding algorithmic results for researchers and practitioners in data analytics, but it is incremental as it builds on existing fieldwork without introducing new methods or benchmarks.
The paper examines how people interpret and validate algorithmic outputs in data analytics, highlighting that meaning is constructed through an iterative dialogue between data, code, and human knowledge, making it difficult to separate human and technical contributions.
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.