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Maschinelles Lernen und Diskriminierung: Probleme und Lösungsansätze
16 references·2 citing·38 second hop
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References

  • 2012CRITICAL QUESTIONS FOR BIG DATA5,449 cited
  • 2000Pandora's Hope: Essays on the Reality of Science Studies5,048 cited
  • 1984The Social Construction of Facts and Artefacts: or How the Sociology of Science and the Sociology of Technology might Benefit Each Other3,887 cited
  • 2016The ethics of algorithms: Mapping the debate3,541 cited
  • 2014Big Data's Disparate Impact3,046 cited
  • 1988The Social Construction of Technological Systems: New Directions in the Sociology and History of Technology2,974 cited
  • 2016Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy2,335 cited
  • 1990Technology is Society Made Durable2,265 cited
  • 2011Data preprocessing techniques for classification without discrimination1,304 cited
  • 1996Bias in computer systems1,203 cited
  • 2017Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy1,058 cited
  • 2017Counterfactual Fairness995 cited
  • 2010Consumer credit-risk models via machine-learning algorithms861 cited
  • 2010Three naive Bayes approaches for discrimination-free classification756 cited
  • 2008Discrimination-aware data mining676 cited

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  • 2013Bias in algorithmic filtering and personalization648 cited
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    • 2020Forbidden knowledge in machine learning reflections on the limits of research and publication21 cited
    • 2022Fairness, Accountability, Sustainability, Transparency (FAST) of Artificial Intelligence in Terms of Hospitality Industry15 cited