Towards Actionable Data Science Systems: An End-user Approach

Abstract

How can we make data science systems more actionable? This dissertation explores this question by placing end-users and their data practices, rather than data scientists and their technical work of building models and algorithms, at the center of data science systems. Inspired by phenomenological views of technical systems from CSCW, HCI, and STS, I use ethnographic and other qualitative methods to understand how participants from four studies worked with data across three settings: craft brewers producing beers, people with visual impairments engaging with image descriptions of their photos on their smartphones, and repair workers repairing broken artifacts. I analyze implications for making data science systems actionable by framing the participants as potential end-users of these systems.

My findings emphasize that actionability in data science systems concerns not just predictions made on mostly given datasets. Actionability in my settings arose from the ongoing work of making data relevant to artifacts and phenomena that end-users engaged with in their practices and settings. I show how this ongoing work of making data relevant was challenging. The properties of artifacts and phenomena were inherently multiple and their relevance was contingent on end-users’ situations. I describe end-users’ data practices as processes of “registering” (making intelligible) a contingent yet coherent set of properties to turn multiple, uncertain artifacts and phenomena into actionable versions.

My dissertation makes several contributions to emerging research on actionability and data science in CSCW, HCI, and STS literature. First, based on my findings, I theorize an approach to data science systems that imagines actionability as driven not so much by data scientists generating predictions, or even by putting humans in the loop, but by placing end-users at the center. Second, my end-user approach to data science systems informs the technical work of data science by proposing requirements for models and algorithms to be accountable not just in their predictions but to end-users’ practices and settings. Third, my dissertation integrates into data science research foundational phenomenological views from CSCW that focus on how technological systems can account for and support end-users in their domains of practice, rather than the other way around.