Refining Policies After Testing

Overview

In Clavata.ai, a policy. defines the guidelines the system follows to analyze and classify content. Policies are customizable, written in clear, human-readable terms, and designed to classify different content types.

After creating a Policy, it’s crucial to test it and then refine it to ensure the AI accurately flags and analyzes content based on your platform’s needs. This is an ongoing process that allows you to make adjustments to ensure your Policies evolve with the needs of your platform. Below are key considerations for refining your Policy.

Align Results to a Policy 

Understand Which Section or Policy Label and Rule Flagged the Content

When a Policy flagged a piece of content, the test results will indicate which Section or Policy Label of your Policy flagged it.

• Locate the Section or Label in your Policy to narrow down what caused the false positive. 
• Investigate further to determine which Rule triggered the flag.
• This helps ensure you’re refining the corresponding Rule(s) and reducing errors efficiently.

If content that you expected to be flagged by your Policy was not detected by any Section, it is considered a False Negative. In this case, there is likely not a Rule in place that captures the intended context.

Refine your Policy 

Address False Positives

False positives occur when False content is incorrectly flagged as True. 

• If a Rule is too broadly written it may over label content.
  • For example, a Rule targeting general vulgar language might flag mild terms like “crap”, when your intention is to flag just explicit vulgarity.
• Amend Rules to target more specific content, or add Conditions to hone it further.
  • For example, specifying the level of vulgarity or content you want to target, or adding Conditions to omit certain contexts that you deem appropriate.

Address False Negatives

False negatives happen when content you wanted labelled is missed by the AI. 

• This can be because the Rules are too narrowly targeting content and is missing grey-area or adjacent contexts. In this case,  broadening the Rules’ scope or adding more Rules to your Policy will help ensure the intended contexts are captured in your Policy.
  • For example, a Rule might target extreme language but miss subtler inappropriate phrases. In this case, broadening the context of your Rule to include slang or casual vulgarity may be a good solution.
• False Negatives can also occur when there simply is not yet a Rule in place that targets the specific contexts.
  • For example, there may be an emerging trend on your platform that was not existent when your Policy was last updated, and now requires Rules to address it.

Experiment with Rule Types

Some contexts can be addressed through different variations of Rule types, so if you are having trouble with a Rule not flagging the expected content, you can experiment with different Rule types:

• Start with the Connecting Conditions like “ANY” and “ALL” to broadly capture multiple signals. These Connecting Conditions offer strong versatility in addressing content issues.
  • For example, use "ALL" when multiple conditions need to be met, and/or use "ANY" when only one condition needs to be met.
• Consider using Context Conditions to gain more granular control over how Signals are detected and flagged. These are great for situational contexts.
• Try Fuzzy Match, which is useful for flagging subversions of certain words.
• Test out Exact Match if you only want specific terms flagged. 

Ensure Correct Labeling and Section Syntax in Tests

The dataset labels directly correspond with Policy’s Label or Section names, which determine the test results.

• Verify that your Policy's Section/Label names align with your User Labels. 
• Your User Labels can be updated in the test result section by clicking on them, or you can update your Policy’s Section names. 
  • However, once your Policy is deployed it is recommended not to update the Section/Label names as these may correspond with logic in the API response. Instead, update your dataset’s User Labels to match your Section names.

Run Another Test 

Run Targeted and Comprehensive Tests

After your initial test and Policy amendments, it is important to run further tests to ensure the changes made do not adversely impact the overall accuracy of your Policy.

• You can run a Test on specific lines of content to ensure the Rule edits flag the intended material.
• Once all issues are addressed, re-run the test on the entirety of your dataset to re-evaluate overall performance and ensure no gaps in detection or unintended false positives remain.

Iterate

Continuous Experimentation and Improvement

Policy refinement is an ongoing process, and should evolve with your online environment. 

• Experiment with different Rule types and configurations.
• Continuously monitor and refine to improve performance.
• Reach out to Clavata.ai if you would like to discuss further how we can support your unique platform needs. 

Need more help? Contact our support team at support@clavata.ai