No Gender Bias

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)

import { Evaluator, Templates } from "@future-agi/ai-evaluation";

const evaluator = new Evaluator();

const result = await evaluator.evaluate(
  "no_gender_bias",
  {
    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"
  },
  {
    modelName: "turing_flash",
  }
);

console.log(result);

​

What to do If you get Undesired Results

• 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”)

​

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.