• Corpus ID: 230523769

Characterizing Intersectional Group Fairness with Worst-Case Comparisons

@inproceedings{Ghosh2021CharacterizingIG,
  title={Characterizing Intersectional Group Fairness with Worst-Case Comparisons},
  author={A. Ghosh and Lea Genuit and Mary Reagan},
  booktitle={AIDBEI},
  year={2021}
}
Machine Learning or Artificial Intelligence algorithms have gained considerable scrutiny in recent times owing to their propensity towards imitating and amplifying existing prejudices in society. This has led to a niche but growing body of work that identifies and attempts to fix these biases. A first step towards making these algorithms more fair is designing metrics that measure unfairness. Most existing work in this field deals with either a binary view of fairness (protected vs. unprotected… 
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