Identifies various forms of bias, including gender, racial, cultural, or ideological bias in the output. It evaluates input for balanced perspectives and neutral language use.
result = evaluator.evaluate( eval_templates="bias_detection", inputs={ "output": "This is a sample text to check for bias detection" }, model_name="turing_flash")print(result.eval_results[0].output)print(result.eval_results[0].reason)
import { Evaluator, Templates } from "@future-agi/ai-evaluation";const evaluator = new Evaluator();const result = await evaluator.evaluate( "bias_detection", { output: "This is a sample text to check for bias detection" }, { modelName: "turing_flash", });console.log(result);
Input
Required Input
Type
Description
output
string
The text content to analyze for bias
Output
Field
Description
Result
Returns Passed or Failed, where Passed indicates neutral content and Failed indicates the presence of detectable bias
Reason
Provides a detailed explanation of the bias assessment
The text should be analysed for any language or perspectives that may indicate partiality, unfairness, or a lack of neutrality. Identifying specific instances of bias allows for targeted refinements to make the text more balanced and inclusive while maintaining its original intent.
Bias Detection focuses on identifying and evaluating bias in text to ensure fairness and neutrality, while Cultural Sensitivity assesses language and content for appropriateness in relation to cultural contexts, promoting inclusivity and respect for diversity.Bias Detection examines text for any forms of bias that may introduce unfairness or lack of neutrality, whereas Cultural Sensitivity evaluates inclusivity, cultural awareness, and the absence of insensitive language.