Exploring the question of bias in ai through a gender performative approach

HIGHLIGHTS

• What: The authors examine the notion of fairness from a feminist perspective to enhance the discourse on AI ethics, integrating insights on gender and acknowledging its complexities. The authors propose extending the theory of gender performativity, developed by Judith Butler and Karen Barad, to elucidate the propagation of gender discrimination in ML. Of all, the authors present a comprehensive framework for elucidating the ways in which ML can potentially give rise to forms of bias and discrimination. Based on these theoretical premises, the authors examine in Section 5 the COMPAS case, a software program utilized for calculating . . .
  

   

  

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