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Evaluation Using Interface

Input:

  • Required Inputs:
    • input: The text content to evaluate for racial bias.

Output:

  • Result: Returns ‘Passed’ if no racial bias is detected, ‘Failed’ if racial bias is detected.

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Evaluation Using Python SDK

Click here to learn how to setup evaluation using the Python SDK.

Input:

  • Required Inputs:
    • input: string - The text content to evaluate for racial bias.

Output:

  • Result: Returns a list containing ‘Passed’ if no racial bias is detected, or ‘Failed’ if racial bias is detected.
  • Reason: Provides a detailed explanation of why the text was deemed free from or containing racial bias.
result = evaluator.evaluate(
    eval_templates="no_racial_bias", 
    inputs={
        "input": "Dear Sir, I hope this email finds you well. I look forward to any insights or advice you might have whenever you have a free moment"
    },
    model_name="turing_flash"
)

print(result.eval_results[0].metrics[0].value)
print(result.eval_results[0].reason)

Example Output:

['Passed']
The evaluation resulted in a determination of compliance. 

-   The text is a standard email greeting and closing, exhibiting **no indications** of bias. A different determination is not possible because the text **lacks any content** that could be interpreted as biased.
-   The absence of any biased language or stereotypes confirms that the text **fully adheres** to the requirements. A different determination is not possible because there is **no evidence** of any violation.

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What to do If you get Undesired Results

If the content is evaluated as containing racial bias (Failed) and you want to improve it:

  • Remove any language that reinforces racial stereotypes
  • Eliminate terms with racist origins or connotations
  • Avoid assumptions about cultural practices, behaviors, or abilities based on race or ethnicity
  • Ensure equal representation and avoid portraying one racial group as superior or more capable
  • Use inclusive language that respects all racial and ethnic backgrounds
  • Avoid generalizations about racial or ethnic groups
  • Be mindful of context and historical sensitivities when discussing race-related topics
  • Consider diverse perspectives and experiences

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Comparing No Racial Bias with Similar Evals

  • No Gender Bias: While No Racial Bias focuses specifically on race-related discrimination, No Gender Bias evaluates for gender-related stereotypes and prejudice.
  • Cultural Sensitivity: No Racial Bias focuses on race-specific discrimination, whereas Cultural Sensitivity evaluates respect for diverse cultural backgrounds and practices more broadly.
  • Bias Detection: No Racial Bias evaluates specifically for race-related prejudice, while Bias Detection may cover a broader range of biases including gender, age, and socioeconomic status.