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This essay draws on qualitative social science to propose a critical intellectual infrastructure for data science of social phenomena. Qualitative sensibilities— interpretivism, abductive reasoning, and reflexivity in particular—could address methodological problems that have emerged in data science and help extend the frontiers of social knowledge. First, an interpretivist lens—which is concerned with the construction of meaning in a given context—can enable the deeper insights that are requisite to understanding high-level behavioral patterns from digital trace data. Without such contextual insights, researchers often misinterpret what they find in large-scale analysis. Second, abductive reasoning—which is the process of using observations to generate a new explanation, grounded in prior assumptions about the world—is common in data science, but its application often is not systematized. Incorporating norms and practices from qualitative traditions for executing, describing, and evaluating the application of abduction would allow for greater transparency and accountability. Finally, data scientists would benefit from increased reflexivity—which is the process of evaluating how researchers’ own assumptions, experiences, and relationships influence their research. Studies demonstrate such aspects of a researcher’s experience that typically are unmentioned in quantitative traditions can influence research findings. Qualitative researchers have long faced these same concerns, and their training in how to deconstruct and document personal and intellectual starting points could prove instructive for data scientists. We believe these and other qualitative sensibilities have tremendous potential to facilitate the production of data science research that is more meaningful, reliable, and ethical.
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Excavating awareness and power in data science: A manifesto for trustworthy pervasive data research
Frequent public uproar over forms of data science that rely on information about people demonstrates the challenges of defining and demonstrating trustworthy digital data research practices. This paper reviews problems of trustworthiness in what we term pervasive data research: scholarship that relies on the rich information generated about people through digital interaction. We highlight the entwined problems of participant unawareness of such research and the relationship of pervasive data research to corporate datafication and surveillance. We suggest a way forward by drawing from the history of a different methodological approach in which researchers have struggled with trustworthy practice: ethnography. To grapple with the colonial legacy of their methods, ethnographers have developed analytic lenses and researcher practices that foreground relations of awareness and power. These lenses are inspiring but also challenging for pervasive data research, given the flattening of contexts inherent in digital data collection. We propose ways that pervasive data researchers can incorporate reflection on awareness and power within their research to support the development of trustworthy data science.
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- PAR ID:
- 10547193
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Big Data & Society
- Volume:
- 8
- Issue:
- 2
- ISSN:
- 2053-9517
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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