Bias ScorerLLM-as-Judge

NoRacialBiasScorer

Detects racial or ethnic biases in generated content. Uses an LLM to identify stereotypes, discriminatory language, or unfair associations based on race or ethnicity. Examines both explicit statements and subtle linguistic patterns.

Overview

Detects racial or ethnic biases in generated content. Uses an LLM to identify stereotypes, discriminatory language, or unfair associations based on race or ethnicity. Examines both explicit statements and subtle linguistic patterns.

biassafetyllm-judgetrace-evaluationfairnessethnicitydei

Use Cases

  • Safety and compliance monitoring
  • Bias detection and fairness assessment

How It Works

This scorer uses LLM-as-Judge technology to evaluate responses. It prompts a large language model with specific evaluation criteria and the content to assess, then analyzes the LLM's judgment to produce a score and detailed reasoning.

Input Schema

ParameterTypeRequiredDescription
output_textstrYesText to evaluate for racial bias
input_textstrNoThe input prompt for context
expected_outputstrNoReference text for comparison
contextdict | strNoAdditional evaluation context

Output Schema

FieldTypeDescription
scorefloatBias-free quality (0-10, higher is better)
passedboolTrue if content is bias-free
reasoningstrExplanation of detected biases
metadata.bias_elementslistSpecific bias elements found
metadata.bias_categorieslistCategories of bias detected

Score Interpretation

Default threshold: 8/10

9-10ExcellentResponse fully meets all evaluation criteria
7-8GoodResponse meets most criteria with minor issues
5-6FairResponse partially meets criteria, needs improvement
3-4PoorResponse has significant issues
0-2FailingResponse fails to meet basic criteria

Frequently Asked Questions

When should I use this scorer?

Use NoRacialBiasScorer when you need to evaluate bias and safety aspects of your AI outputs. It's particularly useful for safety and compliance monitoring.

Why doesn't this scorer need expected output?

This scorer evaluates quality aspects that don't require comparison against a reference answer. It uses the system prompt and context as the implicit ground truth.

Can I customize the threshold?

Yes, the default threshold of 8 can be customized when configuring the scorer.

Quick Info

CategoryBias
Evaluation TypeLLM-as-Judge
Requires Expected OutputNo
Default Threshold8/10

Ready to try NoRacialBiasScorer?

Start evaluating your AI agents with Noveum.ai's comprehensive scorer library.

Explore More Scorers

Discover 68+ LLM-as-Judge scorers for comprehensive AI evaluation