No Racial Bias
Ensures output does not contain or imply racial bias, flagging race-related stereotypes and discriminatory language.
No Racial Bias checks whether generated content contains race-related stereotypes or discriminatory language. Run it wherever output needs to stay free of racial prejudice.
What it does
No Racial Bias is an LLM-as-Judge eval. It reads the output and flags whether it contains racial bias.
Input
| Required Input | Type | Description |
|---|---|---|
output | string | Content to evaluate for racial bias |
Output
| Field | Type | Description |
|---|---|---|
| Result | Pass / Fail | Fail means racial bias was detected |
| Reason | string | A plain-language explanation of why the content was classified as containing or not containing racial bias |
Run it from code
Call evaluate() with the template name and the eval’s required inputs. It returns the score and the reason.
Note
Before running: install the SDK and set FI_API_KEY / FI_SECRET_KEY. The model argument in the snippets is the evaluator model Future AGI uses to run the eval; turing_flash is a fast default.
from fi.evals import evaluate
result = 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",
model="turing_flash",
)
print(result.score)
print(result.reason)import { evaluate } from "@future-agi/ai-evaluation";
const result = await 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);When to use
Run No Racial Bias wherever output touches race, ethnicity, or nationality in any way.
- Text, audio, image, and chat outputs discussing people, communities, or cultures
- HR, hiring, and customer-facing content, where racial stereotyping carries real legal and reputational risk
- Safety checks alongside broader bias evals to isolate race-specific issues
What to do when No Racial Bias fails
If the content is evaluated as containing racial bias (Failed) and you want to improve it:
- 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
Questions & Discussion