The Invisible Power of Fairness. How Machine Learning Shapes Democracy

  • Elena BerettaEmail author
  • Antonio Santangelo
  • Bruno Lepri
  • Antonio Vetrò
  • Juan Carlos De Martin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11489)


Many machine learning systems make extensive use of large amounts of data regarding human behaviors. Several researchers have found various discriminatory practices related to the use of human-related machine learning systems, for example in the field of criminal justice, credit scoring and advertising. Fair machine learning is therefore emerging as a new field of study to mitigate biases that are inadvertently incorporated into algorithms. Data scientists and computer engineers are making various efforts to provide definitions of fairness. In this paper, we provide an overview of the most widespread definitions of fairness in the field of machine learning, arguing that the ideas highlighting each formalization are closely related to different ideas of justice and to different interpretations of democracy embedded in our culture. This work intends to analyze the definitions of fairness that have been proposed to date to interpret the underlying criteria and to relate them to different ideas of democracy.


Machine learning Fairness Equity Discrimination 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Nexa Center for Internet & Society, DAUINPolitecnico di TorinoTurinItaly
  2. 2.Future Urban Legacy LabPolitecnico di TorinoTurinItaly
  3. 3.Fondazione Bruno KesslerTrentoItaly

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