Advancing the Human Work of Data Science
In my doctoral dissertation, I approach data science as a social and situated practice to examine the ‘everyday work’ of data science practitioners—the human choices, collaborative decisions, and organizational imperatives that drive real-world data science practices, but remain often invisible and unaccounted for in data science learning and research. I analyze such forms of work in academic and corporate settings through ethnography and qualitative research. My work has helped to surface the human, collaborative, and organizational dimensions of data science practices, to inform the design and management of accountable data science systems, and to make visible the gaps between the academic training and professional practices of data science.
Drawing on my academic training and professional experiences in the fields of information science, science and technology studies, and computer science, my research style involves actively participating in the everyday work of data science practitioners. This commitment is part of my larger goal to foster an engaged form of interdisciplinary research that addresses and contributes to real-world algorithmic practices.
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.