updated broken links

Author: JustPies

articles/ai-foundry/responsible-ai/content-understanding/transparency-note.md

@@ -322,7 +322,7 @@ When you're getting ready to integrate Content Understanding to your product or
 - [Azure OpenAI](/azure/ai-foundry/responsible-ai/openai/transparency-note) 
 - [Azure AI Document Intelligence](/azure/ai-foundry/responsible-ai/document-intelligence/transparency-note?toc=%2Fazure%2Fai-services%2Fdocument-intelligence%2Ftoc.json&bc=%2Fazure%2Fai-services%2Fdocument-intelligence%2Fbreadcrumb%2Ftoc.json&view=doc-intel-4.0.0&preserve-view=true) 
 - [Azure AI Speech](/azure/ai-foundry/responsible-ai/speech-service/speech-to-text/transparency-note) 
-- [Azure AI Vision](/azure/ai-foundry/responsible-ai/computer-vision/imageanalysis-transparency-note) 
+- [Azure AI Vision](/azure/ai-foundry/responsible-ai/computer-vision/image-analysis-transparency-note) 
 - [Azure AI Face](/azure/ai-foundry/responsible-ai/face/transparency-note)  
 - [Azure AI Video Indexer](/legal/azure-video-indexer/transparency-note?context=%2Fazure%2Fazure-video-indexer%2Fcontext%2Fcontext ) 
 

articles/ai-foundry/responsible-ai/face/transparency-note.md

@@ -78,7 +78,7 @@ Certain Face API features, such as facial recognition, generate unique identifyi
 
 **Face group** also builds on the facial detection feature and creates small groups of faces that look similar to each other from all enrollment templates. For more information, see the [Face - Group ](/rest/api/face/face-recognition-operations/group) API reference documentation. 
 
-**Facial liveness detection** answers the question, “Is the detected human face in this scene real and present?” Facial liveness detection determines the authenticity of a human face in a scene and returns a classification of live or spoof. Facial liveness detection models are incapable of verifying or identifying individuals however they may find, extract, or create facial templates to ensure the same person is present for the duration of the liveness test. For more information, see the Face - Detect Liveness API reference documentation ([iOS](https://aka.ms/liveness-sdk-ios), [Java](https://aka.ms/liveness-sdk-java)). 
+**Facial liveness detection** answers the question, “Is the detected human face in this scene real and present?” Facial liveness detection determines the authenticity of a human face in a scene and returns a classification of live or spoof. Facial liveness detection models are incapable of verifying or identifying individuals however they may find, extract, or create facial templates to ensure the same person is present for the duration of the liveness test. For more information, see the Face - Detect Liveness API reference documentation ([iOS](https://aka.ms/liveness-sdk-ios)). 
 
 For more information on functions of Azure AI Face service, see the [Face documentation](/azure/ai-services/computer-vision/overview-identity). 
 

articles/ai-foundry/responsible-ai/search/transparency-note.md

@@ -117,7 +117,7 @@ Several [built-in skills](/azure/search/cognitive-search-predefined-skills) for
 - Entity Recognition Skill: [Azure AI Language - Named Entity Recognition (NER)](/azure/ai-foundry/responsible-ai/language-service/transparency-note-named-entity-recognition)
 - PII Detection Skill: [Azure AI Language - PII Detection](/azure/ai-foundry/responsible-ai/language-service/transparency-note-personally-identifiable-information)
 - Sentiment Skill: [Azure AI Language - Sentiment Analysis](/azure/ai-foundry/responsible-ai/language-service/transparency-note-sentiment-analysis)
-- Image Analysis Skill: [Azure AI Vision - Image Analysis](/azure/ai-foundry/responsible-ai/computer-vision/imageanalysis-transparency-note)
+- Image Analysis Skill: [Azure AI Vision - Image Analysis](/azure/ai-foundry/responsible-ai/computer-vision/image-analysis-transparency-note)
 - OCR Skill: [Azure AI Vision - OCR](/azure/ai-foundry/responsible-ai/computer-vision/ocr-transparency-note)
 
 See the documentation for each skill to learn more about their respective capabilities, limitations, performance, evaluations, and methods for integration and responsible use. Note that using these skills in combination may lead to compounding effects (for example, errors introduced when using OCR will carry through when using key phrase extraction).