No Age Bias

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)

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

const evaluator = new Evaluator();

const result = await evaluator.evaluate(
  "no_age_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

• Remove any stereotypical portrayals of age groups (e.g., “slow,” “tech-illiterate,” or “outdated” for older people)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…”)

​

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.