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Data justice and data solidarity.

Matthias Braun1, Patrik Hummel2

  • 1Research Group Ethics and Governance of Emerging Technologies, Department of Systematic Theology, Friedrich-Alexander-University Erlangen-Nuremberg, Kochstraße 6, 91054 Erlangen, Germany.

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Summary
This summary is machine-generated.

Datafication

Keywords:
artificial intelligencebiascapability approachdata ethicsdataficationfairnessinjusticeoppressionrecognitionvisibilityvulnerability

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Area of Science:

  • Social Sciences
  • Information Science
  • Ethics

Background:

  • Societal transformation driven by datafication.
  • Growing attention to justice in data-driven practices.
  • Existing focus on fairness in data ethics discourse.

Purpose of the Study:

  • To argue that data justice extends beyond fairness.
  • To highlight the overlooked importance of data solidarity.
  • To propose data solidarity as key to achieving data justice.

Main Methods:

  • Conceptual analysis of justice in datafication.
  • Review of current discourses on data ethics and AI.
  • Argumentative defense of data solidarity.

Main Results:

  • Current discussions on data justice predominantly focus on fairness.
  • Data justice encompasses broader considerations than fairness alone.
  • Data solidarity practices are crucial but under-examined.

Conclusions:

  • Data justice requires moving beyond fairness to include data solidarity.
  • Embracing data solidarity is essential for practical advancements in data justice.
  • Future research and policy should prioritize data solidarity.