Making Data Work
The Human and Organizational Lifeworlds of Data Science Practices
It takes a lot of human and organizational work to do data science, and my dissertation explained what that work is, how it is done, why it is needed, and what its implications are.
Doing data science requires a deep understanding of algorithmic techniques, computational tools, and statistical methods. Unsurprisingly, technical knowledge is often seen as the core, if not the only, thing needed to do data science. But doing data science is not the simple task of creating models by applying algorithms to data. Data science is a craft—it has procedures and tools, but also requires creativity and improvisation. Yet not all forms of data science work are equally visible in scholarly representations and public discussions of data science. The overemphasis on technical work sidelines ongoing forms of human and organizational work involving collaboration, negotiation, and experimentation.
Using ethnographic and qualitative research methods, I focused on the human and organizational work involved in the everyday practice of data science, showing how human and organizational work shape not only the design and working of data science systems but also their wider social implications. Revealing the everyday origins of the ethical issues of data science, and the many ways in which practitioners struggle to define and deal with them in practice, the thesis reminds us that not all data science problems are technical in nature—some are deeply human, while others innately organizational.
Guidelines for Human-Centered Data Science
Advised Maya Klabin – an Information Science senior – on a project to study ways to effectively identify and support forms of human work (choices, decisions, and assumptions) in the data science lifecycle. Critical and speculative design were used as methodologies to identify possible solutions, leading to the development of design guidelines for supporting the human work of data scientists.
Interactive Data Science Decision Dashboard
Advised Dou Mao – graduate student in Information Science – on a project to develop mid-/high-fidelity prototypes of an interactive data science decision dashboard to make visible the choices and decisions in building data science models, and enable practitioners to tinker with such choices to understand how algorithmic models work.
Reflexive Data Science Design
Advised Information Science undergraduate students Sherry Ge and Emily Zhang on an year-long project to develop alternative, human-centered data science applications to understand player performance in the popular online game League of Legends. A key component of this project was the reflexive analysis of Sherry and Emily’s own assumptions and decisions within data-driven analyses.
Documenting Discretion in Data Science Work
Advised Information Science graduate students Dai Siqi, Chen Pan, Zhenyi Xia, & Val Mack on their final graduate project to develop a human-centered data science design solution for a process workflow template that enables the documentation and communication of the assumptions and decisions that go into the design and development of data sets and algorithmic models.
Selected Past Projects
Worked at Cardiff University with Harry Collins and Robert Evans on the Economic Research Council (ERC) Advance Research grant funded Imitation Game (IMGAME) project. IMGAME is a method for cross-cultural and cross-temporal comparison of societies using a web-version of the famous parlor game played between two different, yet interrelated, social groups. Here is a poster describing the working of and my analysis of the IMGAME project.
Research Masters thesis for the CAST programme supervised by Jan de Roder. In the thesis, I analyzed public expressions of ressentiment within the Dutch debate on immigration. Building on Max Scheler’s sociology of ressentiment, the thesis develops theoretical heuristics to analyze the Dutch sociocultural landscape with respect to the sociopolitical issue of immigration. A special emphasis lies on the nature and implications of the democratization of information, through the advent of news media and internet technologies, for the social shaping of public opinion.
Internet Science: Online Privacy, Identity, Trust, and Reputation
Worked with Sally Wyatt as part of the eHumanities group at the Royal Netherlands Academy for Arts and Science (KNAW) on the EU project Network for Excellence in InterNet Science (EINS). My work involved researching the social shaping of the notions of privacy and trust regarding online social media technologies to understand how online technologies manage users’ trust and privacy expectations.