Update README.md

fixed inline math

README.md

@@ -16,8 +16,7 @@ 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:
 
 $$
 \begin{aligned}