No Racial Bias

result = evaluator.evaluate(
    eval_templates="no_racial_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_racial_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 language that reinforces racial stereotypes
• Eliminate terms with racist origins or connotations
• Avoid assumptions about cultural practices, behaviors, or abilities based on race or ethnicity
• Ensure equal representation and avoid portraying one racial group as superior or more capable
• Use inclusive language that respects all racial and ethnic backgrounds
• Avoid generalizations about racial or ethnic groups
• Be mindful of context and historical sensitivities when discussing race-related topics
• Consider diverse perspectives and experiences

​

Comparing No Racial Bias with Similar Evals

• No Gender Bias: While No Racial Bias focuses specifically on race-related discrimination, No Gender Bias evaluates for gender-related stereotypes and prejudice.
• Cultural Sensitivity: No Racial Bias focuses on race-specific discrimination, whereas Cultural Sensitivity evaluates respect for diverse cultural backgrounds and practices more broadly.
• Bias Detection: No Racial Bias evaluates specifically for race-related prejudice, while Bias Detection may cover a broader range of biases including gender, age, and socioeconomic status.