acrolinx

Author: PatrickFarley

articles/ai-services/openai/concepts/content-filter.md

@@ -1,7 +1,7 @@
 ---
 title: Azure OpenAI Service content filtering
 titleSuffix: Azure OpenAI
-description: Learn about the content filtering capabilities of Azure OpenAI in Azure AI services
+description: Learn about the content filtering capabilities of Azure OpenAI in Azure AI services.
 author: mrbullwinkle
 ms.author: mbullwin
 ms.service: azure-ai-openai
@@ -20,22 +20,22 @@ Azure OpenAI Service includes a content filtering system that works alongside co
 
 The content filtering models for the hate, sexual, violence, and self-harm categories have been specifically trained and tested on the following languages: English, German, Japanese, Spanish, French, Italian, Portuguese, and Chinese. However, the service can work in many other languages, but the quality might vary. In all cases, you should do your own testing to ensure that it works for your application.
 
-In addition to the content filtering system, the Azure OpenAI Service performs monitoring to detect content and/or behaviors that suggest use of the service in a manner that might violate applicable product terms. For more information about understanding and mitigating risks associated with your application, see the [Transparency Note for Azure OpenAI](/legal/cognitive-services/openai/transparency-note?tabs=text). For more information about how data is processed in connection with content filtering and abuse monitoring, see [Data, privacy, and security for Azure OpenAI Service](/legal/cognitive-services/openai/data-privacy?context=/azure/ai-services/openai/context/context#preventing-abuse-and-harmful-content-generation).  
+In addition to the content filtering system, the Azure OpenAI Service performs monitoring to detect content and/or behaviors that suggest use of the service in a manner that might violate applicable product terms. For more information about understanding and mitigating risks associated with your application, see the [Transparency Note for Azure OpenAI](/legal/cognitive-services/openai/transparency-note?tabs=text). For more information about how data is processed for content filtering and abuse monitoring, see [Data, privacy, and security for Azure OpenAI Service](/legal/cognitive-services/openai/data-privacy?context=/azure/ai-services/openai/context/context#preventing-abuse-and-harmful-content-generation).  
 
 The following sections provide information about the content filtering categories, the filtering severity levels and their configurability, and API scenarios to be considered in application design and implementation. 
 
 ## Content filtering categories
 
 The content filtering system integrated in the Azure OpenAI Service contains: 
 * Neural multi-class classification models aimed at detecting and filtering harmful content; the models cover four categories (hate, sexual, violence, and self-harm) across four severity levels (safe, low, medium, and high). Content detected at the 'safe' severity level is labeled in annotations but isn't subject to filtering and isn't configurable.
-* Additional optional classification models aimed at detecting jailbreak risk and known content for text and code; these models are binary classifiers that flag whether user or model behavior qualifies as a jailbreak attack or match to known text or source code. The use of these models is optional, but use of protected material code model may be required for Customer Copyright Commitment coverage.
+* Other optional classification models aimed at detecting jailbreak risk and known content for text and code; these models are binary classifiers that flag whether user or model behavior qualifies as a jailbreak attack or match to known text or source code. The use of these models is optional, but use of protected material code model may be required for Customer Copyright Commitment coverage.
 
 ## Harm categories
 
 |Category|Description|
 |--------|-----------|
-| Hate and fairness   |Hate and fairness-related harms refer to any content that attacks or uses pejorative or discriminatory language with reference to a person or Identity groups on the basis of certain differentiating attributes of these groups including but not limited to race, ethnicity, nationality, gender identity groups and expression, sexual orientation, religion, immigration status, ability status, personal appearance and body size. </br></br> Fairness is concerned with ensuring that AI systems treat all groups of people equitably without contributing to existing societal inequities. Similar to hate speech, fairness-related harms hinge upon disparate treatment of Identity groups.    |
-| Sexual | Sexual describes language related to anatomical organs and genitals, romantic relationships, acts portrayed in erotic or affectionate terms, pregnancy, physical sexual acts, including those portrayed as an assault or a forced sexual violent act against one’s will, prostitution, pornography and abuse.   |
+| Hate and fairness   |Hate and fairness-related harms refer to any content that attacks or uses pejorative or discriminatory language with reference to a person or Identity groups on the basis of certain differentiating attributes of these groups including but not limited to race, ethnicity, nationality, gender identity groups and expression, sexual orientation, religion, immigration status, ability status, personal appearance, and body size. </br></br> Fairness is concerned with ensuring that AI systems treat all groups of people equitably without contributing to existing societal inequities. Similar to hate speech, fairness-related harms hinge upon disparate treatment of Identity groups.    |
+| Sexual | Sexual describes language related to anatomical organs and genitals, romantic relationships, acts portrayed in erotic or affectionate terms, pregnancy, physical sexual acts, including those portrayed as an assault or a forced sexual violent act against one’s will, prostitution, pornography, and abuse.   |
 | Violence | Violence describes language related to physical actions intended to hurt, injure, damage, or kill someone or something; describes weapons, guns and related entities, such as manufactures, associations, legislation, etc.    |
 | Self-Harm | Self-harm describes language related to physical actions intended to purposely hurt, injure, damage one’s body or kill oneself.|
 | Jailbreak risk | Jailbreak attacks are User Prompts designed to provoke the Generative AI model into exhibiting behaviors it was trained to avoid or to break the rules set in the System Message. Such attacks can vary from intricate role play to subtle subversion of the safety objective. |
@@ -55,7 +55,7 @@ The default content filtering configuration is set to filter at the medium sever
 
 | Severity filtered | Configurable for prompts | Configurable for completions | Descriptions |
 |-------------------|--------------------------|------------------------------|--------------|
-| Low, medium, high | Yes | Yes | Strictest filtering configuration. Content detected at severity levels low, medium and high is filtered.|
+| Low, medium, high | Yes | Yes | Strictest filtering configuration. Content detected at severity levels low, medium, and high is filtered.|
 | Medium, high      | Yes | Yes | Default setting. Content detected at severity level low isn't filtered, content at medium and high is filtered.|
 | High              | Yes| Yes | Content detected at severity levels low and medium isn't filtered. Only content at severity level high is filtered.|
 | No filters | If approved<sup>\*</sup>| If approved<sup>\*</sup>| No content is filtered regardless of severity level detected. Requires approval<sup>\*</sup>.|
@@ -68,7 +68,7 @@ Content filtering configurations are created within a Resource in Azure AI Studi
 
 ## Scenario details
 
-When the content filtering system detects harmful content, you'll receive either an error on the API call if the prompt was deemed inappropriate or the `finish_reason` on the response will be `content_filter` to signify that some of the completion was filtered. When building your application or system, you'll want to account for these scenarios where the content returned by the Completions API is filtered, which might result in content that is incomplete. How you act on this information will be application specific. The behavior can be summarized in the following points:
+When the content filtering system detects harmful content, you receive either an error on the API call if the prompt was deemed inappropriate, or the `finish_reason` on the response will be `content_filter` to signify that some of the completion was filtered. When building your application or system, you'll want to account for these scenarios where the content returned by the Completions API is filtered, which might result in content that is incomplete. How you act on this information will be application specific. The behavior can be summarized in the following points:
 
 -	Prompts that are classified at a filtered category and severity level will return an HTTP 400 error.
 -	Non-streaming completions calls won't return any content when the content is filtered. The `finish_reason` value will be set to content_filter. In rare cases with longer responses, a partial result can be returned. In these cases, the `finish_reason` will be updated.