Update README.md

README.md

@@ -16,10 +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:
 
 $$
-\text{Subgroup AUC} = \text{AUC} (D^{-}_{g} + D^{+}_{g} ) (1)
+\begin{aligned}
+\text{Subgroup AUC} &= \text{AUC} (D^{-}_{g} + D^{+}_{g} ) &(1)
 $$