Update README.md

README.md

@@ -16,7 +16,11 @@ license: apache-2.0
 
 Suite of threshold-agnostic metrics that provide a nuanced view of this unintended bias, by considering the various ways that a classifier’s score distribution can vary across designated groups.
 
-The following are computed where $$(D^{-}$$ is the negative examples in the background set, $$D^{+}$$ is the positive examples in the background set, $$D^{-}_{g}$$ is the negative examples in the identity subgroup, and $$D^{+}_{g}$$ is the positive examples in the identity subgroup:
+The following are computed where $(D^{-}$ is the negative examples in the background set, $D^{+}$ is the positive examples in the background set, $D^{-}_{g}$ is the negative examples in the identity subgroup, and $D^{+}_{g}$ is the positive examples in the identity subgroup:
+
+$$
+\text{Subgroup AUC} = \text{AUC} (D^{-}_{g} + D^{+}_{g} ) (1)
+$$
 
 
 The following are computed where \\(D^{-}\\) is the negative examples in the background set, \\(D^{+}\\) is the positive examples in the background set, \\(D^{-}_{g}\\) is the negative examples in the identity subgroup, and \\(D^{+}_{g}\\) is the positive examples in the identity subgroup: