Free JD Bias Checker

Scan a job description for exclusionary language — gendered terms, age indicators, ableist phrases, and other patterns that narrow your candidate pipeline before anyone applies.

Why bias in job descriptions matters

Research consistently shows that the language in a job description affects who applies. Gendered wording, age-coded language, and exclusionary phrasing all signal who “belongs” in a role — and who doesn't. The result is a narrower, less diverse pipeline before the screening process even begins.

A 2011 study published in the Journal of Personality and Social Psychology found that women were less likely to apply for jobs described with masculine-coded words like “aggressive,” “dominant,” and “competitive” — even when they were qualified. The words you choose have measurable impact on your applicant pool.

Common bias categories in job descriptions

Gendered language

Masculine-coded words: aggressive, dominant, competitive, driven, independent. Feminine-coded words: nurturing, supportive, collaborative, empathetic. Neither set is inherently bad — but an imbalance signals an expectation about who fills the role.

Age indicators

Terms like “digital native,” “recent graduate,” “young and energetic,” or “5-7 years of experience” can be proxies for age discrimination. Focus on skills, not age.

Ableist language

Physical requirements that aren't genuinely essential, or mental health terms used as metaphors (“crazy fast,” “OCD about code quality”).

Frequently asked questions

What does the JD Bias Checker scan for?
It scans for gendered language (terms with masculine or feminine connotations), age indicators, ableist language, exclusionary phrases, and cultural-fit terminology that can deter diverse candidates. Each flag includes an explanation of why the term is problematic and a suggested alternative.
Is this a comprehensive bias audit?
No. This tool catches common exclusionary language patterns, but bias in job descriptions goes deeper than word choice. The requirements you list, the qualifications you demand, and the structure of the role itself can all introduce bias that a text scanner won't catch. Use this as a first pass, not a complete audit.
What should I do after scanning?
Review each flag. Some may be false positives — contextual language that isn't problematic in your specific usage. For real flags, rewrite the language using the suggestions provided. Then have someone from an underrepresented group review the revised JD before publishing.

Bias-free JD is step one. Consistent screening is step two.

TalentDraft applies the same structured criteria to every candidate, reducing the bias that creeps in during manual review.