Merge pull request #185899 from lgayhardt/azmlresponsible012022

responsible ML -> AI

Author: v-ccolin

articles/machine-learning/concept-differential-privacy.md

@@ -2,8 +2,8 @@
 title: Differential privacy in machine learning (preview)
 titleSuffix: Azure Machine Learning
 description: Learn what differential privacy is and how differentially private systems preserve data privacy. 
-author: ssalgadodev
-ms.author: ssalgado 
+author: lgayhardt
+ms.author: lagayhar 
 ms.date: 10/21/2021
 services: machine-learning
 ms.service: machine-learning

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

@@ -6,16 +6,16 @@ services: machine-learning
 ms.service: machine-learning
 ms.subservice: enterprise-readiness
 ms.topic: conceptual
-ms.author: ssalgado
-author: ssalgadodev
+ms.author: lagayhar
+author: lgayhardt
 ms.date: 10/21/2021
 ms.custom: responsible-ml
 #Customer intent: As a data scientist, I want to learn about machine learning fairness and how to assess and mitigate unfairness in machine learning models.
 ---
 
 # Machine learning fairness (preview)
 
-Learn about machine learning fairness and how the [Fairlearn](https://fairlearn.github.io/) open-source Python package can help you assess and mitigate unfairness issues in machine learning models. 
+Learn about machine learning fairness and how the [Fairlearn](https://fairlearn.github.io/) open-source Python package can help you assess and mitigate unfairness issues in machine learning models.
 
 ## What is machine learning fairness?
 

articles/machine-learning/concept-open-source.md

@@ -50,7 +50,7 @@ To learn how to use Ray RLLib with Azure Machine Learning, see the [how to train
 
 Training a single or multiple models requires the visualization and inspection of desired metrics to make sure the model performs as expected. You can [use TensorBoard in Azure Machine Learning to track and visualize experiment metrics](./how-to-monitor-tensorboard.md)
 
-## Responsible ML: Privacy and fairness
+## Responsible AI: Privacy and fairness
 
 ### Preserve data privacy with differential privacy
 

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

@@ -1,25 +1,25 @@
 ---
-title: What is responsible machine learning (preview)
+title: What is responsible AI (preview)
 titleSuffix: Azure Machine Learning
-description: Learn what responsible machine learning is and how to use it with Azure Machine Learning to understand models, protect data and control the model lifecycle.
+description: Learn what responsible AI is and how to use it with Azure Machine Learning to understand models, protect data and control the model lifecycle.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: enterprise-readiness
 ms.topic: conceptual
-ms.author: ssalgado
-author: ssalgadodev
+ms.author: lagayhar
+author: lgayhardt
 ms.date: 10/21/2021
 ms.custom: responsible-ml
-#Customer intent: As a data scientist, I want to know learn what responsible machine learning is and how I can use it in Azure Machine Learning.
+#Customer intent: As a data scientist, I want to know learn what responsible AI is and how I can use it in Azure Machine Learning.
 ---
 
-# What is responsible machine learning? (preview)
+# What is responsible AI? (preview)
 
-In this article, you'll learn what responsible machine learning (ML) is and ways you can put it into practice with Azure Machine Learning.
+In this article, you'll learn what responsible AI is and ways you can put it into practice with Azure Machine Learning.
 
-## Responsible machine learning principles
+## Responsible AI principles
 
-Throughout the development and use of AI systems, trust must be at the core. Trust in the platform, process, and models. At Microsoft, responsible machine learning encompasses the following values and principles:
+Throughout the development and use of AI systems, trust must be at the core. Trust in the platform, process, and models. At Microsoft, responsible AI with regards tomachine learning encompasses the following values and principles:
 
 - Understand machine learning models
   - Interpret and explain model behavior
@@ -30,7 +30,7 @@ Throughout the development and use of AI systems, trust must be at the core. Tru
 - Control the end-to-end machine learning process
   - Document the machine learning lifecycle with datasheets
 
-:::image type="content" source="media/concept-responsible-ml/responsible-ml-pillars.png" alt-text="Responsible ML pillars - interpretability, differential privacy, homomorphic encryption, audit trail - Azure Machine Learning":::
+:::image type="content" source="media/concept-responsible-ml/responsible-ml-pillars.png" alt-text="Responsible AI pillars - interpretability, differential privacy, homomorphic encryption, audit trail - Azure Machine Learning":::
 
 As artificial intelligence and autonomous systems integrate more into the fabric of society, it's important to proactively make an effort to anticipate and mitigate the unintended consequences of these technologies.
 

articles/machine-learning/how-to-machine-learning-interpretability-aml.md

@@ -7,7 +7,7 @@ ms.service: machine-learning
 ms.subservice: enterprise-readiness
 ms.author: mithigpe
 author: minthigpen
-ms.reviewer: Luis.Quintanilla
+ms.reviewer: lagayhar
 ms.date: 10/21/2021
 ms.topic: how-to
 ms.custom: devx-track-python, responsible-ml, mktng-kw-nov2021

articles/machine-learning/media/concept-responsible-ml/responsible-ml-pillars.png

@@ -191,7 +191,7 @@ The information in the rest of this document provides information on what featur
 | Labeler Portal                                                            | GA               | YES       | N/A        |
 | Labeling using private workforce                                          | GA               | YES       | N/A        |
 | ML assisted labeling (Image classification and object detection)           | Public Preview   | YES       | N/A        |
-| **Responsible ML** |    | | |
+| **Responsible AI** |    | | |
 | Explainability in UI                                                       | Public Preview   | NO        | N/A        |
 | Differential privacy SmartNoise toolkit                                    | OSS              | NO        | N/A        |
 | custom tags in Azure Machine Learning to implement datasheets              | GA               | NO        | N/A        |

articles/machine-learning/reference-machine-learning-cloud-parity.md

@@ -168,9 +168,9 @@
             href: concept-component.md
           - name: MLflow
             href: concept-mlflow.md
-          - name: Responsible ML
+          - name: Responsible AI
             items:
-              - name: Responsible ML overview
+              - name: Responsible AI overview
                 displayName: responsible,ml
                 href: concept-responsible-ml.md
               - name: Model interpretability