While data science learning and research focus almost-exclusively on the seemingly technical aspects of data science (code, algorithms, data structures, etc.), what remain less-visible are the “human” forms of data science work comprising assumptions, choices, and decisions that data scientists navigate as they move through the messy terrain of data, carving – what in retrospect looks like – a linear narrative of knowledge discovery. I treat the human data science work not separate from the more-visible technical work, but as work that is deeply intertwined with algorithms and technicalities. In my work, I study such forms of human data science work ethnographically in two separate contexts: (a) academic data science (one-year of ethnographic research conducted in graduate-level machine learning and data mining classrooms in a US university), and (b) corporate data science (6-months of ethnographic research conducted at a US tech firm working as a data scientist). For this workshop, I focus on the question of ‘stakes’ in data science. Specifically, I present my findings to showcase how commonly identified sociotechnical ‘stakes’ in data science (e.g., ontology, marginalization, and bias) appear to and get instantiated within everyday data science practices of data processing, modeling, analysis, visualization, and evaluation.
Critical data studies research has made visible the ‘design-use gap’—users (as people most affected by data science systems) often do not have a say in the system’s design. Much discussion thus focuses on the role and place of user participation in data science practices. In this piece, however, I focus on already-existing forms of participatory work in corporate data science practices. Corporate projects are highly participatory in nature (though not in the way we often define and expect participation). These projects necessitate diverse forms of work on the part of multiple personnel such as data scientists, project managers, business analysts, product managers, and business executives. Unpacking the collaborative work in corporate data science projects as forms of participation provides us with a different perspective on the design-use gap, helping us focus on different forms of participation in corporate data science practices.
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.
When discussing the difficulties faced by interdisciplinary researchers, we often talk about issues of methodology (how to study what you want to study), of scope (how to limit the mess in our research), of access (how to interpret the work done in different disciplines), of audience (who do we want to talk to), and of profession (what kind of job can we get or should aim for). All of these are important issues. However, I argue that there exists another set of pertinent, often overlooked, challenges faced by interdisciplinary researchers. These challenges are, in my opinion, especially – but not exclusively – salient for junior interdisciplinary researchers who constantly straddle between two or more disciplines. These arise within everyday interpretations of the professional identity of these researchers. That is: not only the ways in which my colleagues interpret what I do, how I do it, and why I do it, but also what my colleagues expect of me and my work when they hear that I am from x or y department or that I am doing a PhD in x or y field. In this paper, I talk about how I have experienced this specific challenge in my own lived experiences of being an “Information Science PhD student.” In particular, I will describe this challenge within the explicit context of my own movement from the field of Science & Technology Studies (STS) to the field of Information Science (IS).