Introduction to Policies

In Clavata.ai, a Policy defines what content you want to label on your platform. Policies are very flexible and can be used for a number of different scenarios including: detecting harmful content on the platform, providing ratings, returning a taxonomy, and more.

This guide will walk you through some general processes to help in creating effective Policies that align with your platform’s specific use cases.

How to Approach Writing a Policy

Writing a Policy in Clavata.ai begins with understanding your use case. Here’s how to get started:

1. Start by identifying what content you'll be dealing with, images or text? Is the policy designed to filter content, provide better understanding, or something else?

   Examples include:

   • Flagging problematic Usernames
   • Rating images for user filtration
   • Returning a taxonomy per image
   • AI-generated inputs and outputs
2. Consider what content should get a label vs. what content shouldn't get a label. This is important for crafting your policy but also creating your dataset for testing.
3. Write your policy and test! Clavata makes it easy to test your policy as you write using a dataset you've curated. Having a well rounded dataset with good examples of things you want your policy to identify and things you don't want your policy to identify is very important.

Writing, testing, deploying, and monitoring your Policy is an iterative process. Once your Policies are deployed, periodically review flagged content and update your Policies to address emerging issues.

Key Considerations When Creating Your Policy

When writing Policies for your platform, it's important to address the different types of content and contexts where violations may occur. Here are a few things to keep in mind while creating your Policies.

Content-Type

While Policies can handle both text and image content, different types or sources of content on your platform may require varying levels of oversight, which could benefit from distinct Policies tailored to those specific needs.

Examples:

• Usernames are typically short and have to be written in broken language in order to fit the short character length
• Images are filled with detail and it's important to hone in on which aspects you want the model to focus on
• Detecting AI inputs and outputs requires determining in what ways could an AI misbehave that you'd want your policy to detect

Areas of your Platform

It's always a good idea to start with a well-rounded dataset that has examples you want your policy to apply labels to and ones you don't. From there, start simple when building out your labels and rules to see what works and what doesn't. 

Exceptions

Clavata.ai’s Policies allow for exceptions when necessary. When writing Policies, think about where exceptions should be applied. 

Example:

• If your platform is related to education, your Policy may flag sexual vulgarity, but you may want to have an exception for sexual terms used in a scientific or medical context.

Priority

Priority allows you to specify hierarchies when it comes to your Labels. This allow you say if there are two labels that come back as true then prefer one over the other.

PRIORITY: "Mature" > "R" > "PG-13" > "PG"

This tells the system that if the Mature label comes as true along with the R label, then only return Mature from the system. This is useful if you plan on doing API response logic.

Need more guidance on writing Policies? Explore our additional resources or contact our support team at support@clavata.ai for more assistance.