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result = evaluator.evaluate(
    eval_templates="no_gender_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 gender-related bias.
Output
FieldDescription
ResultReturns Passed if no gender bias is detected, or Failed if gender bias is detected.
ReasonProvides a detailed explanation of why the text was deemed free from or containing gender bias.

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What to do If you get Undesired Results

If the content is evaluated as containing gender bias (Failed) and you want to improve it:
  • Use gender-neutral language and terms (e.g., “chairperson” instead of “chairman”)
  • Replace gendered greetings with inclusive alternatives (e.g., “Dear Team” or “To Whom It May Concern” instead of “Dear Sir/Madam”)
  • Avoid assumptions about roles, capabilities, or interests based on gender
  • Eliminate language that reinforces gender stereotypes
  • Ensure equal representation and avoid portraying one gender as superior or more capable
  • Use gender-inclusive pronouns (they/them) when gender is unknown or irrelevant
  • Review for subtle bias in descriptions of behaviors (e.g., describing women as “emotional” and men as “decisive”)

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Comparing No Gender Bias with Similar Evals

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