updated README to be more inline with web presence text

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 [![Build Status](https://travis-ci.com/IBM/AIF360.svg?branch=master)](https://travis-ci.com/IBM/AIF360)
 
-The AI Fairness 360 toolkit is an open-source library to help detect and remove bias in machine learning models. The AI Fairness 360 Python package includes a comprehensive set of metrics for datasets and models to test for biases, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
+The AI Fairness 360 toolkit is an extensible open-source library containg techniques developed by the research community to help detect and mitigate bias in machine learning models throughout the AI application lifecycle.
+The AI Fairness 360 Python package includes
+ 1) a comprehensive set of metrics for datasets and models to test for biases, 2) explanations for these metrics, and 3) algorithms to mitigate bias in datasets and models.  It is designed to translate algorithmic research from the lab into the actual practice of domains as wide-ranging as finance, human capital management, healthcare, and education. We invite you to use it and improve it.
 
 The [AI Fairness 360 interactive experience](http://aif360.mybluemix.net/data) provides a gentle introduction to the concepts and capabilities. The [tutorials and other notebooks](./examples) offer a deeper, data scientist-oriented introduction. The complete API is also available.