Merge pull request #5494 from PatrickFarley/rai-auto-migration

Rai auto migration

Author: v-dirichards

articles/ai-foundry/responsible-ai/agents/data-privacy-security.md

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 # Data, privacy, and security for Azure AI Agent Service
 
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 Azure AI Agent Service is a fully managed service designed to empower developers to securely build, deploy, and scale high-quality, and extensible AI agents without needing to manage the underlying compute and storage resources. Azure AI Agent Service integrates **models, tools and technology** and enables you to extend agents with knowledge from connected sources (such as Bing Search, SharePoint, Fabric, Azure Blob storage, and licensed data) and actions using tools such as Azure Logic Apps, Azure Functions, OpenAPI 3.0 specified tools and Code Interpreter
 
 > [!NOTE]

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

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 # Transparency Note for Azure Agent Service
 
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 ## What is a Transparency Note?
 
 An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is deployed. Creating a system that is fit for its intended purpose requires an understanding of how the technology works, what its capabilities and limitations are, and how to achieve the best performance. Microsoft’s Transparency Notes are intended to help you understand how our AI technology works, the choices system owners can make that influence system performance and behavior, and the importance of thinking about the whole system, including the technology, the people, and the environment. You can use Transparency Notes when developing or deploying your own system or share them with the people who will use or be affected by your system.

articles/ai-foundry/responsible-ai/anomaly-detector/ad-transparency-note.md

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 # Transparency note: Anomaly Detector
 
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 Anomaly Detector is an AI service that enables you to monitor and detect abnormalities in time series data. This article provides transparency information to help you understand how the service works, its capabilities, limitations, and responsible use considerations.
 
 ## What is a Transparency Note?

articles/ai-foundry/responsible-ai/anomaly-detector/characteristics-and-limitations.md

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 # Characteristics and limitations of Anomaly Detector
 
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 In this section, we'll review what accuracy means for Anomaly Detector and how to assess it for your context.
 
 > [!NOTE]

articles/ai-foundry/responsible-ai/anomaly-detector/data-privacy-security.md

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 # Data and privacy for Anomaly Detector
 
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 > [!NOTE]
 > While the information in this article only refers to Anomaly Detector, most of the concepts described apply to both Anomaly Detector and Azure AI Metrics Advisor. Metrics Advisor is built on top of the core technology from Anomaly Detector.
 

articles/ai-foundry/responsible-ai/anomaly-detector/guidance-integration-responsible-use.md

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 # Guidance for integration and responsible use with Anomaly Detector
 
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 Microsoft works to help customers responsibly develop and deploy solutions that use Anomaly Detector. Our principled approach upholds personal agency and dignity by considering the AI system's fairness, reliability and safety, privacy and security, inclusiveness, transparency, and human accountability. These considerations reflect our commitment to developing responsible AI.
 
 > [!NOTE]

articles/ai-foundry/responsible-ai/anomaly-detector/transparency-note.md

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 # Transparency Note for Anomaly Detector
 
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 ## What is a Transparency Note?
 
 An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is deployed. Creating a system that is fit for its intended purpose requires an understanding of how the technology works, its capabilities and limitations, and how to achieve the best performance.

articles/ai-foundry/responsible-ai/clu/clu-characteristics-and-limitations.md

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 # Characteristics and limitations for using conversational language understanding
 
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 Performance with conversational language understanding (CLU) varies based on the scenario, input data, and enabled features. The following sections are designed to help the reader understand key concepts about performance as they apply to CLU.
 
 ## Understand and measure performance

articles/ai-foundry/responsible-ai/clu/clu-data-privacy-security.md

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 # Data and privacy for conversational language understanding
 
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 This article provides high-level information about how data is processed by conversational language understanding (CLU). You're responsible for your use and implementation of this technology, which includes complying with all laws and regulations that apply to you. For example, it's your responsibility to:
 
 * Understand where your data is processed and stored by CLU to meet regulatory obligations for your application.

articles/ai-foundry/responsible-ai/clu/clu-guidance-integration-responsible-use.md

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 # Guidance for integration and responsible use with conversational language understanding
 
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 Microsoft works to help customers responsibly develop and deploy solutions by using conversational language understanding (CLU). Our principled approach upholds personal agency and dignity by considering the AI system's:
 
 - Fairness, reliability, and safety.