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Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science.

Gina Neff1, Anissa Tanweer2, Brittany Fiore-Gartland3

  • 11 Oxford Internet Institute, University of Oxford , Oxford, United Kingdom .

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|June 21, 2017
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Summary
This summary is machine-generated.

This study bridges critiques and practices in data science to foster more ethical data science. Engaging critical data studies and understanding daily practices will improve both scholarly critiques and data science applications.

Keywords:
critical data studiesdata for gooddata scienceethicsqualitative methodstheory

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Area of Science:

  • Social Sciences
  • Computer Science

Background:

  • Critical data studies offer critiques of data science.
  • Data science practice involves complex, context-dependent work.

Purpose of the Study:

  • To bridge critical data studies and data science practice for more ethical data science.
  • To identify social and organizational arrangements for ethical data science.

Main Methods:

  • Qualitative research with academic data scientists, 'data for good' projects, and engineering teams.
  • Ethnographic vignettes from two large field sites.

Main Results:

  • Identified four common critiques in critical data studies: data's interpretive nature, context-inextricability, sociomaterial mediation, and value negotiation.
  • Developed concepts: communication centrality, collective sense-making, data as starting points, and data as stories.

Conclusions:

  • Integrating social scientific and humanistic expertise advances data science and critical data studies.
  • Practitioners should use critical data studies insights to build organizational arrangements for ethical data science.