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

Co-authored-by: Lauryn Gayhardt <[email protected]>

Author: ShawnJackson

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

@@ -25,7 +25,7 @@ Two common types of AI-caused harms are:
 
 - **Harm of allocation**: An AI system extends or withholds opportunities, resources, or information for certain groups. Examples include hiring, school admissions, and lending, where a model might be better at picking good candidates among a specific group of people than among other groups.
 
-- **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.
+- **Harm of quality-of-service**: 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.