edit pass: more-concept-articles

Author: ShawnJackson

articles/machine-learning/concept-causal-inference.md

@@ -14,9 +14,9 @@ ms.custom: responsible-ml, event-tier1-build-2022
 
 # Make data-driven policies and influence decision-making (preview)
 
-Although machine learning models are powerful in identifying patterns in data and making predictions, they offer little support for estimating how the real-world outcome changes in the presence of an intervention. 
+Machine learning models are powerful in identifying patterns in data and making predictions. But they offer little support for estimating how the real-world outcome changes in the presence of an intervention. 
 
-Practitioners have become increasingly focused on using historical data to inform their future decisions and business interventions. For example, how would the revenue be affected if a corporation pursues a new pricing strategy? Would a new medication improve a patient's condition, all else equal?
+Practitioners have become increasingly focused on using historical data to inform their future decisions and business interventions. For example, how would the revenue be affected if a corporation pursued a new pricing strategy? Would a new medication improve a patient's condition, all else equal?
 
 The *causal inference* component of the [Responsible AI dashboard](concept-responsible-ai-dashboard.md) addresses these questions by estimating the effect of a feature on an outcome of interest on average, across a population or a cohort, and on an individual level. It also helps construct promising interventions by simulating feature responses to various interventions and creating rules to determine which population cohorts would benefit from an intervention. Collectively, these functionalities allow decision-makers to apply new policies and effect real-world change.
 

articles/machine-learning/concept-counterfactual-analysis.md

@@ -20,7 +20,7 @@ Standard interpretability techniques approximate a machine learning model or ran
 
 Such an analysis helps in disentangling the impact of correlated features in isolation. It also helps you get a more nuanced understanding of how much of a feature change is needed to see a model decision flip for classification models and a decision change for regression models.
 
-The counterfactual analysis and what-if component of the [Responsible AI dashboard](concept-responsible-ai-dashboard.md) has two functions:
+The *counterfactual analysis and what-if* component of the [Responsible AI dashboard](concept-responsible-ai-dashboard.md) has two functions:
 
 - Generate a set of examples with minimal changes to a particular point such that they change the model's prediction (showing the closest data points with opposite model predictions).
 - Enable users to generate their own what-if perturbations to understand how the model reacts to feature changes.

articles/machine-learning/concept-data-analysis.md

@@ -16,7 +16,7 @@ ms.custom: responsible-ml, event-tier1-build-2022
 
 Machine learning models "learn" from historical decisions and actions captured in training data. As a result, their performance in real-world scenarios is heavily influenced by the data they're trained on. When feature distribution in a dataset is skewed, it can cause a model to incorrectly predict data points that belong to an underrepresented group or to be optimized along an inappropriate metric. 
 
-For example, while a model was training an AI system for predicting house prices, the training set was representing 75 percent of newer houses that have less than median prices. As a result, it was much less accurate in successfully identifying more expensive historic houses. The fix was to add older and expensive houses to the training data and augment the features to include insights about historic value. That data augmentation improved results.
+For example, while a model was training an AI system for predicting house prices, the training set was representing 75 percent of newer houses that had less than median prices. As a result, it was much less accurate in successfully identifying more expensive historic houses. The fix was to add older and expensive houses to the training data and augment the features to include insights about historic value. That data augmentation improved results.
 
 The data explorer component of the [Responsible AI dashboard](concept-responsible-ai-dashboard.md) helps visualize datasets based on predicted and actual outcomes, error groups, and specific features. It helps you identify issues of overrepresentation and underrepresentation and to see how data is clustered in the dataset. Data visualizations consist of aggregate plots or individual data points.
 

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

@@ -27,14 +27,14 @@ Two common types of AI-caused harms are:
 
 - **Quality-of-service harm**: An AI system doesn't work as well for one group of people as it does for another. For example, a voice recognition system might fail to work as well for women as it does for men.
 
-To reduce unfair behavior in AI systems, you have to assess and mitigate these harms. The model overview component of the [Responsible AI dashboard](concept-responsible-ai-dashboard.md) contributes to the identification stage of the model lifecycle by generating model performance metrics for your entire dataset and your identified cohorts of data. It generates these metrics across subgroups identified in terms of sensitive features or sensitive attributes.
+To reduce unfair behavior in AI systems, you have to assess and mitigate these harms. The *model overview* component of the [Responsible AI dashboard](concept-responsible-ai-dashboard.md) contributes to the identification stage of the model lifecycle by generating model performance metrics for your entire dataset and your identified cohorts of data. It generates these metrics across subgroups identified in terms of sensitive features or sensitive attributes.
 
 >[!NOTE]
 > Fairness is a socio-technical challenge. Quantitative fairness metrics don't capture many aspects of fairness, such as justice and due process. Also, many quantitative fairness metrics can't all be satisfied simultaneously. 
 >
 > The goal of the Fairlearn open-source package is to enable humans to assess the impact and mitigation strategies. Ultimately, it's up to the humans who build AI and machine learning models to make trade-offs that are appropriate for their scenarios.
 
-In this component of the Responsible AI dashboard, fairness is conceptualized through an approach known as *group fairness*, which asks: Which groups of individuals are at risk for experiencing harm? The term *sensitive features* suggests that the system designer should be sensitive to these features when assessing group fairness. 
+In this component of the Responsible AI dashboard, fairness is conceptualized through an approach known as *group fairness*. This approach asks: "Which groups of individuals are at risk for experiencing harm?" The term *sensitive features* suggests that the system designer should be sensitive to these features when assessing group fairness. 
 
 During the assessment phase, fairness is quantified through *disparity metrics*. These metrics can evaluate and compare model behavior across groups either as ratios or as differences. The Responsible AI dashboard supports two classes of disparity metrics: