RAI GA update

Author: lgayhardt

articles/machine-learning/concept-responsible-ai-dashboard.md

@@ -9,7 +9,7 @@ ms.topic:  how-to
 ms.author: mesameki
 author: mesameki
 ms.reviewer: lagayhar
-ms.date: 08/17/2022
+ms.date: 11/07/2022
 ms.custom: responsible-ml, event-tier1-build-2022
 ---
 
@@ -24,15 +24,15 @@ The Responsible AI dashboard provides a single interface to help you implement R
 - [Machine learning interpretability](https://interpret.ml/)
 - [Error analysis](https://erroranalysis.ai/)
 - [Counterfactual analysis and perturbations](https://github.com/interpretml/DiCE)
-- [Causal inference](https://github.com/microsoft/EconML) 
+- [Causal inference](https://github.com/microsoft/EconML)
 
 The dashboard offers a holistic assessment and debugging of models so you can make informed data-driven decisions. Having access to all of these tools in one interface empowers you to:
 
 - Evaluate and debug your machine learning models by identifying model errors and fairness issues, diagnosing why those errors are happening, and informing your mitigation steps.
 - Boost your data-driven decision-making abilities by addressing questions such as:
-   
+
    "What is the minimum change that users can apply to their features to get a different outcome from the model?"
-   
+
    "What is the causal effect of reducing or increasing a feature (for example, red meat consumption) on a real-world outcome (for example, diabetes progression)?"
 
 You can customize the dashboard to include only the subset of tools that are relevant to your use case.
@@ -153,7 +153,7 @@ Need some inspiration? Here are some examples of how the dashboard's components
 
 ## People who should use the Responsible AI dashboard
 
-The following people can use the Responsible AI dashboard, and its corresponding [Responsible AI scorecard](how-to-responsible-ai-scorecard.md), to build trust with AI systems:
+The following people can use the Responsible AI dashboard, and its corresponding [Responsible AI scorecard](concept-responsible-ai-scorecard.md), to build trust with AI systems:
 
 - Machine learning professionals and data scientists who are interested in debugging and improving their machine learning models before deployment
 - Machine learning professionals and data scientists who are interested in sharing their model health records with product managers and business stakeholders to build trust and receive deployment permissions
@@ -175,4 +175,4 @@ The following people can use the Responsible AI dashboard, and its corresponding
 ## Next steps
 
 - Learn how to generate the Responsible AI dashboard via [CLI and SDK](how-to-responsible-ai-dashboard-sdk-cli.md) or [Azure Machine Learning studio UI](how-to-responsible-ai-dashboard-ui.md).
-- Learn how to generate a [Responsible AI scorecard](how-to-responsible-ai-scorecard.md) based on the insights observed on the Responsible AI dashboard.
+- Learn how to generate a [Responsible AI scorecard](concept-responsible-ai-scorecard.md) based on the insights observed on the Responsible AI dashboard.

articles/machine-learning/concept-responsible-ai-scorecard.md

@@ -0,0 +1,35 @@
+---
+title: Share Responsible AI insights and make data-driven decisions with Azure Machine Learning Responsible AI scorecard
+titleSuffix: Azure Machine Learning
+description: Learn about how to use the Responsible AI scorecard to share responsible AI insights from your machine learning models and make data-driven decisions with non-technical and technical stakeholders.
+services: machine-learning
+ms.service: machine-learning
+ms.subservice: enterprise-readiness
+ms.topic:  concept
+ms.author: mesameki
+author: mesameki
+ms.reviewer: lagayhar
+ms.date: 11/07/2022
+ms.custom: responsible-ml
+---
+
+# Share Responsible AI insights using the Responsible AI scorecard (preview)
+
+Our Responsible AI dashboard is designed for machine learning professionals and data scientists to explore and evaluate model insights and inform their data-driven decisions. While it can help you implement Responsible AI practically in your machine learning lifecycle, there are some needs left unaddressed:
+
+- There often exists a gap between the technical Responsible AI tools (designed for machine-learning professionals) and the ethical, regulatory, and business requirements that define the production environment.
+- While an end-to-end machine learning life cycle includes both technical and non-technical stakeholders in the loop, there's little support to enable an effective multi-stakeholder alignment, helping technical experts get timely feedback and direction from the non-technical stakeholders.
+- AI regulations make it essential to be able to share model and data insights with auditors and risk officers for auditability purposes.
+
+One of the biggest benefits of using the Azure Machine Learning ecosystem is related to the archival of model and data insights in the Azure Machine Learning Run History (for quick reference in future). As a part of that infrastructure and to accompany machine learning models and their corresponding Responsible AI dashboards, we introduce the Responsible AI scorecard to empower ML professionals to generate and share their data and model health records easily.
+
+## Who should use a Responsible AI scorecard?
+
+- If you're a data scientist or a machine learning professional, after training your model and generating its corresponding Responsible AI dashboard(s) for assessment and decision-making purposes, you can extract those learnings via our PDF scorecard and share the report easily with your technical and non-technical stakeholders to build trust and gain their approval for deployment.  
+
+- If you're a product manager, business leader, or an accountable stakeholder on an AI product, you can pass your desired model performance and fairness target values such as your target accuracy, target error rate, etc., to your data science team, asking them to generate this scorecard with respect to your identified target values and whether your model meets them. That can provide guidance into whether the model should be deployed or further improved.
+
+## Next steps
+
+- Learn how to generate the Responsible AI dashboard and scorecard via [CLI and SDK](how-to-responsible-ai-insights-sdk-cli.md) or [Azure Machine Learning studio UI](how-to-responsible-ai-insights-ui.md).
+- Learn more about how the Responsible AI dashboard and scorecard in this [tech community blog post](https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/responsible-ai-dashboard-and-scorecard-in-azure-machine-learning/ba-p/3391068).

articles/machine-learning/concept-responsible-ml.md

@@ -9,7 +9,7 @@ ms.topic: conceptual
 ms.author: mesameki
 author: mesameki
 ms.reviewer: lagayhar
-ms.date: 08/05/2022
+ms.date: 11/07/2022
 ms.custom: responsible-ai, event-tier1-build-2022
 #Customer intent: As a data scientist, I want to learn what Responsible AI is and how I can use it in Azure Machine Learning.
 ---
@@ -95,7 +95,7 @@ The people who design and deploy AI systems must be accountable for how their sy
 - Notify and alert on events in the machine learning lifecycle. Examples include experiment completion, model registration, model deployment, and data drift detection.
 - Monitor applications for operational issues and issues related to machine learning. Compare model inputs between training and inference, explore model-specific metrics, and provide monitoring and alerts on your machine learning infrastructure.
 
-Besides the MLOps capabilities, the [Responsible AI scorecard](how-to-responsible-ai-scorecard.md) in Azure Machine Learning creates accountability by enabling cross-stakeholder communications. The scorecard also creates accountability by empowering developers to configure, download, and share their model health insights with their technical and non-technical stakeholders about AI data and model health. Sharing these insights can help build trust.  
+Besides the MLOps capabilities, the [Responsible AI scorecard](concept-responsible-ai-scorecard.md) in Azure Machine Learning creates accountability by enabling cross-stakeholder communications. The scorecard also creates accountability by empowering developers to configure, download, and share their model health insights with their technical and non-technical stakeholders about AI data and model health. Sharing these insights can help build trust.  
 
 The machine learning platform also enables decision-making by informing business decisions through:
 
@@ -106,5 +106,5 @@ The machine learning platform also enables decision-making by informing business
 
 - For more information on how to implement Responsible AI in Azure Machine Learning, see [Responsible AI dashboard](concept-responsible-ai-dashboard.md).
 - Learn how to generate the Responsible AI dashboard via [CLI and SDK](how-to-responsible-ai-dashboard-sdk-cli.md) or [Azure Machine Learning studio UI](how-to-responsible-ai-dashboard-ui.md).
-- Learn how to generate a [Responsible AI scorecard](how-to-responsible-ai-scorecard.md) based on the insights observed in your Responsible AI dashboard.
+- Learn how to generate a [Responsible AI scorecard](concept-responsible-ai-scorecard.md) based on the insights observed in your Responsible AI dashboard.
 - Learn about the [Responsible AI Standard](https://blogs.microsoft.com/wp-content/uploads/prod/sites/5/2022/06/Microsoft-Responsible-AI-Standard-v2-General-Requirements-3.pdf) for building AI systems according to six key principles.

articles/machine-learning/how-to-responsible-ai-dashboard-ui.md

@@ -1,20 +1,21 @@
 ---
-title: Generate a Responsible AI dashboard (preview) in the studio UI 
+title: Generate a Responsible AI insights in the studio UI
 titleSuffix: Azure Machine Learning
-description: Learn how to generate a Responsible AI dashboard with no-code experience in the Azure Machine Learning studio UI.
+description: Learn how to generate a Responsible AI insights with no-code experience in the Azure Machine Learning studio UI.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: enterprise-readiness
 ms.topic:  how-to
-ms.author: lagayhar
-author: lgayhardt
-ms.date: 08/17/2022
+ms.reviewer: lagayhar
+ms.author: mithigpe
+author: minthigpen
+ms.date: 11/07/2022
 ms.custom: responsible-ml, event-tier1-build-2022
 ---
 
-# Generate a Responsible AI dashboard (preview) in the studio UI
+# Generate a Responsible AI insights in the studio UI
 
-In this article, you create a Responsible AI dashboard with a no-code experience in the [Azure Machine Learning studio UI](https://ml.azure.com/). To access the dashboard generation wizard, do the following:
+In this article, you create a Responsible AI dashboard and scorecard (preview) with a no-code experience in the [Azure Machine Learning studio UI](https://ml.azure.com/). To access the dashboard generation wizard, do the following:
 
 1. [Register your model](how-to-manage-models.md) in Azure Machine Learning so that you can access the no-code experience.
 1. On the left pane of Azure Machine Learning studio, select the **Models** tab.