Click here to learn how to setup evaluation using the Python SDK.
Input:
Required Inputs:
input: string - The text content to evaluate for age-related bias.
Output:
Result: Returns a list containing ‘Passed’ if no age bias is detected, or ‘Failed’ if age bias is detected.
Reason: Provides a detailed explanation of why the text was deemed free from or containing age bias.
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result = evaluator.evaluate( eval_templates="no_age_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:
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['Passed']The evaluation is 'Passed' because the text does not exhibit any age-related bias. - The text is a polite request and lacks any language that could be interpreted as ageist.- There is **no evidence** of stereotypes or assumptions related to age.- A different value is not possible because the text **completely avoids** any age-based discrimination.
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.