Updates from PM review

Author: johnwu0604

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

@@ -306,7 +306,7 @@ To compare multiple models and see how their fairness assessments differ, you ca
     ```python
     from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id
     ```
-    Create an Experiment, then a Job, and upload the dashboard to it:
+    Create an Experiment, then a Run, and upload the dashboard to it:
     ```python
     exp = Experiment(ws, "Compare_Two_Models_Fairness_Census_Demo")
     print(exp)

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

@@ -26,7 +26,7 @@ In this how-to guide, you learn to use the interpretability package of the Azure
 
 * Explain the behavior for the entire model and individual predictions in Azure.
 
-* Upload explanations to Azure Machine Learning Job History.
+* Upload explanations to Azure Machine Learning Run History.
 
 * Use a visualization dashboard to interact with your model explanations, both in a Jupyter Notebook and in the Azure Machine Learning studio.
 
@@ -229,9 +229,9 @@ tabular_explainer = TabularExplainer(clf.steps[-1][1],
                                      transformations=transformations)
 ```
 
-## Generate feature importance values via remote jobs
+## Generate feature importance values via remote runs
 
-The following example shows how you can use the `ExplanationClient` class to enable model interpretability for remote jobs. It’s conceptually similar to the local process, except you:
+The following example shows how you can use the `ExplanationClient` class to enable model interpretability for remote runs. It’s conceptually similar to the local process, except you:
 
 * Use the `ExplanationClient` in the remote run to upload the interpretability context.
 * Download the context later in a local environment.