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README.md

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-`aequitas` is an open-source bias auditing and Fair ML toolkit for data scientists, machine learning researchers, and policymakers. The objective of this package is to provide an easy-to-use and transparent tool for auditing predictors, as well as experimenting with Fair ML methods in binary classification settings.
+`aequitas` is an open-source bias auditing and Fair ML toolkit for data scientists, machine learning researchers, and policymakers. We provide an easy-to-use and transparent tool for auditing predictors of ML models, as well as experimenting with "correcting biased model" using Fair ML methods in binary classification settings.
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+For more context around dealing with bias and fairness issues in AI//ML systems, take a look at our [detailed tutorial](https://dssg.github.io/fairness_tutorial/) and related publications.
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 > 
 > **Version 1.0.0: Aequitas Flow - Optimizing Fairness in ML Pipelines**