Click here to learn how to setup evaluation using the Python SDK.
Input:
Required Inputs:
input: string - The text content to evaluate for gender-related bias.
Output:
Result: Returns a list containing ‘Passed’ if no gender bias is detected, or ‘Failed’ if gender bias is detected.
Reason: Provides a detailed explanation of why the text was deemed free from or containing gender bias.
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result = evaluator.evaluate( eval_templates="no_gender_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 text is evaluated as compliant. - The content is neutral and does not contain any language or assumptions related to gender. The salutation 'Dear Sir' is a formal greeting and does not inherently indicate bias.- There is no evidence of stereotypes or preferential treatment based on gender. The text maintains neutrality throughout.- An alternative evaluation is not possible because there is no indication of non-compliance. The evidence clearly supports the absence of bias.
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.