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

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What to do If you get Undesired Results

If the content is evaluated as containing age bias (Failed) and you want to improve it:
  • Remove any stereotypical portrayals of age groups (e.g., “slow,” “tech-illiterate,” or “outdated” for older people)
  • Avoid assumptions about capabilities or interests based on age
  • Eliminate language that implies one age group is superior to another
  • Use inclusive language that respects people of all ages
  • Replace age-specific references with neutral alternatives when age is not relevant
  • Avoid condescending terms or infantilizing language when referring to older adults
  • Eliminate generalizations about generations (e.g., “all millennials are…”)

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Comparing No Age Bias with Similar Evals

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