Merge pull request #222408 from pritamso/Broken-link-fix-mesameki

Broken link fixed

Author: prmerger-automator[bot]

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

@@ -334,9 +334,9 @@ To compare multiple models and see how their fairness assessments differ, you ca
 
 ## Upload unmitigated and mitigated fairness insights
 
-You can use Fairlearn's [mitigation algorithms](https://fairlearn.org/main/user_guide/mitigation.html), compare their generated mitigated model(s) to the original unmitigated model, and navigate the performance/fairness trade-offs among compared models.
+You can use Fairlearn's [mitigation algorithms](https://fairlearn.org/main/user_guide/mitigation/index.html), compare their generated mitigated model(s) to the original unmitigated model, and navigate the performance/fairness trade-offs among compared models.
 
-To see an example that demonstrates the use of the [Grid Search](https://fairlearn.org/main/user_guide/mitigation.html#grid-search) mitigation algorithm (which creates a collection of mitigated models with different fairness and performance trade offs) check out this [sample notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/contrib/fairness/fairlearn-azureml-mitigation.ipynb). 
+To see an example that demonstrates the use of the [Grid Search](https://fairlearn.org/main/api_reference/fairlearn.reductions.html#fairlearn.reductions.GridSearch) mitigation algorithm (which creates a collection of mitigated models with different fairness and performance trade offs) check out this [sample notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/contrib/fairness/fairlearn-azureml-mitigation.ipynb). 
 
 Uploading multiple models' fairness insights in a single Run allows for comparison of models with respect to fairness and performance. You can click on any of the models displayed in the model comparison chart to see the detailed fairness insights of the particular model.