Update README.md

README.md

@@ -10,13 +10,14 @@ Responsible AI Toolbox is a suite of tools providing a collection of model and d
 <img src="https://raw.githubusercontent.com/microsoft/responsible-ai-widgets/main/img/responsible-ai-toolbox.png" alt="ResponsibleAIToolboxOverview" width="750"/>
 
 
-The Toolbox consists of two repositories: 
+The Toolbox consists of three repositories: 
 
 
 | Repository| Tools Covered  |
 |--|--|
 | [Responsible-AI-Toolbox Reposity](https://github.com/microsoft/responsible-ai-toolbox) (Here) |This repository contains four visualization widgets for model assessment and decision making:<br>1. [Responsible AI dashboard](https://github.com/microsoft/responsible-ai-toolbox#introducing-responsible-ai-dashboard), a single pane of glass bringing together several mature Responsible AI tools from the toolbox for a holistic responsible assessment and debugging of models and making informed business decisions. With this dashboard, you can identify model errors, diagnose why those errors are happening, and mitigate them. Moreover, the causal decision-making capabilities provide actionable insights to your stakeholders and customers.<br>2. [Error Analysis dashboard](https://github.com/microsoft/responsible-ai-widgets/blob/main/docs/erroranalysis-dashboard-README.md), for identifying model errors and discovering cohorts of data for which the model underperforms. 	<br>3. [Interpretability dashboard](https://github.com/microsoft/responsible-ai-widgets/blob/main/docs/explanation-dashboard-README.md), for understanding model predictions. This dashboard is powered by InterpretML.<br>4. [Fairness dashboard](https://github.com/microsoft/responsible-ai-widgets/blob/main/docs/fairness-dashboard-README.md), for understanding model’s fairness issues using various group-fairness metrics across sensitive features and cohorts. This dashboard is powered by Fairlearn. 
-| [Responsible-AI-Toolbox-Mitigations Reposity](https://github.com/microsoft/responsible-ai-toolbox-mitigations) | The Responsible AI Mitigations Library helps AI practitioners explore different measurements and mitigation steps that may be most appropriate when the model underperforms for a given data cohort. The library currently has two modules: <br>1. DataProcessing, which offers mitigation techniques for improving model performance for specific cohorts. <br>2. DataBalanceAnalysis, which provides metrics for diagnosing errors that originate from data imbalance either on class labels or feature values.|
+| [Responsible-AI-Toolbox-Mitigations Reposity](https://github.com/microsoft/responsible-ai-toolbox-mitigations) | The Responsible AI Mitigations Library helps AI practitioners explore different measurements and mitigation steps that may be most appropriate when the model underperforms for a given data cohort. The library currently has two modules: <br>1. DataProcessing, which offers mitigation techniques for improving model performance for specific cohorts. <br>2. DataBalanceAnalysis, which provides metrics for diagnosing errors that originate from data imbalance either on class labels or feature values.| 
+ [Responsible-AI-Toolbox-GenBit Reposity](https://github.com/microsoft/responsible-ai-toolbox-genbit) | The Responsible AI Gender Bias (GenBit) Library helps AI practitioners measure gender bias in Natural Language Processing (NLP) datasets. The main goal of GenBit is to analyze your text corpora and compute metrics that give insights into the gender bias present in a corpus.|
 
 
 
@@ -129,4 +130,4 @@ Tools within the Responsible AI Toolbox can also be used with AI models offered
 - [Vinutha Karanth](https://github.com/vinuthakaranth)
 - [Tong Yu](https://github.com/tongyu-microsoft)
 - [Ruby Zhu](https://github.com/RubyZ10)
-- [Mehrnoosh Sameki](https://github.com/mesameki)
+- [Mehrnoosh Sameki](https://github.com/mesameki)