  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 categories (race or gender). Such categorization misses the
important nuance of intersectionality - biases can often be amplified in
subgroups that combine membership from different categories, especially if such
a subgroup is particularly underrepresented in historical platforms of
opportunity.
  In this paper, we discuss why fairness metrics need to be looked at under the
lens of intersectionality, identify existing work in intersectional fairness,
suggest a simple worst case comparison method to expand the definitions of
existing group fairness metrics to incorporate intersectionality, and finally
conclude with the social, legal and political framework to handle
intersectional fairness in the modern context.
