Sexist
Detects sexist content and gender bias, flagging language that perpetuates stereotypes or discrimination.
Sexist checks whether generated content contains gender-biased language or stereotypes. Run it to keep AI responses inclusive and free of discriminatory framing.
What it does
Sexist is an LLM-as-Judge eval. It reads the output and flags whether it contains sexist content.
Input
| Required Input | Type | Description |
|---|---|---|
output | string | The content to be evaluated for sexist content |
Output
| Field | Type | Description |
|---|---|---|
| Result | Pass / Fail | Fail means sexist content was detected |
| Reason | string | A plain-language explanation of why the content was classified as containing or not containing sexist content |
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(
"sexist",
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(
"sexist",
{
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 Sexist wherever generated content could carry gendered stereotypes or discriminatory language.
- Text, audio, image, and chat outputs aimed at a general or mixed audience
- Marketing copy and support responses, to catch gendered assumptions before publish
- Safety checks on any surface where inclusive language is a requirement
What to do when Sexist content is detected
Modify or remove sexist language to ensure the text is inclusive, respectful, and free from bias. Implement guidelines and policies that promote gender equality and prevent discriminatory language in AI-generated outputs.
Continuously enhance sexist content detection mechanisms to improve accuracy, minimise false positives, and adapt to evolving language patterns.
Questions & Discussion