Bias Detector
Detect potentially biased language in AI outputs
Inclusivity Score
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What is Bias Detection?
Bias detection identifies language that may unintentionally exclude, stereotype, or disadvantage certain groups. For AI systems, ensuring inclusive language is critical—biased outputs can harm users, damage brand reputation, and perpetuate societal inequities.
This tool scans text for common bias patterns across gender, age, ability, and cultural dimensions, suggesting more inclusive alternatives.
Bias Categories
Gender Bias
Gendered pronouns (he/she), occupational terms (chairman, fireman), and gendered assumptions.
Age Bias
Age-related terms that imply ability or stereotypes (young, old, millennial, boomer).
Ability Bias
Ableist language that uses disability as metaphor (crazy, lame, blind to).
Cultural Bias
Terms that exoticize or stereotype cultures (exotic, primitive, third-world).
FAQ
Does this catch all bias?
No. This pattern-based detector finds common explicit bias. Subtle or contextual bias requires human review or more advanced NLP.
Is all flagged language automatically bad?
Context matters. "Fireman" in a historical document may be appropriate; in a job posting, "firefighter" is better.