Characterizing Intersectional Group Fairness with Worst-Case Comparisons. (arXiv:2101.01673v5 [cs.LG] UPDATED)

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 groups) or
politically defined …

arxiv case fairness