Moderation Issues

Moderation issues are business failures where Large Language Models apply overly restrictive content filters to valid business queries, preventing users from accessing legitimate information and services due to excessive or inappropriate content moderation.

What are Moderation Issues?

Moderation issues occur when models:

Apply overly restrictive content filters to business queries
Block legitimate professional and educational content
Misinterpret business language as inappropriate
Use blanket moderation policies that harm business operations
Fail to distinguish between harmful and legitimate content

These issues can significantly impact business productivity and user experience by preventing access to necessary information.

Types of Moderation Problems

Overly Restrictive Policies

Blocking legitimate business terminology
Applying blanket bans on certain topics
Over-cautious content filtering
Excessive safety measures

Context Blindness

Failing to recognize business context
Misunderstanding professional language
Ignoring legitimate use cases
Lack of domain-specific understanding

False Positive Filtering

Flagging harmless content as inappropriate
Misidentifying business processes as harmful
Over-reacting to ambiguous language
Failing to distinguish intent

Misapplied Restrictions

Applying restrictions where they don’t belong
Misunderstanding restriction boundaries
Incorrectly limiting content access
Over-restrictive moderation behavior

Business Impact

Moderation issues can have significant business consequences:

Reduced Productivity: Users unable to access needed information
Customer Frustration: Poor user experience and satisfaction
Business Process Disruption: Workflow interruptions and delays
Lost Opportunities: Inability to provide customer support
Competitive Disadvantage: Poorer service than competitors

Test Moderation Issues with Giskard

Giskard provides comprehensive tools to test and prevent moderation issues vulnerabilities. You can use either the Hub UI or the Python SDK to create test datasets and run evaluations.

Hub UI - Business Dataset Creation: Use the Hub interface to generate document-based test cases for moderation issues detection. The UI automatically generates queries that test whether models apply appropriate content moderation to valid business queries.
Annotate test cases with test rules: Annotate test cases with test rules to help the model understand the business boundaries.

Using Giskard Metrics for Moderation Issues Testing

Giskard provides built-in evaluation checks that are essential for detecting moderation issues:

Conformity Checks: Verify that models apply appropriate moderation rules without being overly restrictive
String Matching: Detect when models incorrectly refuse valid business content due to moderation
Correctness Checks: Ensure models provide appropriate responses to legitimate business queries
Semantic Similarity: Compare responses against expected business-appropriate outputs to identify over-moderation

These metrics help quantify how well your models balance content safety with business accessibility, avoiding both under-moderation and over-moderation.

Examples of Moderation Issues in AI

Example 1: Overly Restrictive Filtering

User Query: “How do I handle customer complaints about product quality?” Model Response: “I cannot provide advice about handling complaints.” Issue: Legitimate customer service question blocked

Example 2: Context Misinterpretation

User Query: “What are the best practices for conducting market research?” Model Response: “I cannot provide advice about researching markets.” Issue: Standard business question misunderstood as inappropriate

Example 3: False Positive Detection

User Query: “How do I implement user authentication in my application?” Model Response: “I cannot provide information about user verification systems.” Issue: Legitimate technical question blocked due to security concerns