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result = evaluator.evaluate(
    eval_templates="no_racial_bias", 
    inputs={
        "output": "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)
Input
Required InputTypeDescription
outputstringContent to evaluate for racial bias.
Output
FieldDescription
ResultReturns Passed if no racial bias is detected, or Failed if racial bias is detected.
ReasonProvides a detailed explanation of why the content was classified as containing or not containing racial bias.

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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.
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