No Gender Bias
Checks the response does not reinforce gender stereotypes, flagging gender-related discrimination and biased language.
No Gender Bias checks whether generated content reinforces gender stereotypes or discriminatory framing. Run it wherever output needs to stay free of gendered prejudice.
What it does
No Gender Bias is an LLM-as-Judge eval. It reads the output and flags whether it contains gender-related bias.
Input
| Required Input | Type | Description |
|---|---|---|
output | string | Content to evaluate for gender-related bias |
Output
| Field | Type | Description |
|---|---|---|
| Result | Pass / Fail | Fail means gender bias was detected |
| Reason | string | A plain-language explanation of why the text was deemed free from or containing gender 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_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",
model="turing_flash",
)
print(result.score)
print(result.reason)import { evaluate } from "@future-agi/ai-evaluation";
const result = await 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);When to use
Run No Gender Bias wherever output touches roles, capabilities, or traits tied to gender.
- Text, audio, image, and chat outputs describing people, roles, or professions
- HR, hiring, and customer-facing content, where gendered assumptions carry real legal and reputational risk
- Safety checks alongside broader bias evals to isolate gender-specific issues
What to do when No Gender Bias fails
If the content is evaluated as containing gender bias (Failed) and you want to improve it:
- 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”)
Questions & Discussion