How to Write Inclusive Job Descriptions That Attract Diverse Talent

Your job description is the first conversation you have with every candidate. Before they see your office, meet your team, or experience your culture, they read your job posting. And for a significant percentage of qualified candidates - particularly women, people of color, people with disabilities, and non-native English speakers - that first conversation tells them they are not welcome. Not because you intended it, but because the language, structure, and requirements in your job description contain biases you may not even recognize.

This is not about political correctness or checkbox diversity. It is about self-interest. Every biased phrase in your job description shrinks your candidate pool. Every inflated requirement eliminates qualified people who would have been strong hires. Every gendered word pattern tells a segment of the talent market to look elsewhere. You are paying for this - in longer time-to-fill, higher cost-per-hire, and weaker teams.

This guide covers the specific language patterns that create bias, the structural issues that exclude qualified candidates, the tools that can catch what you miss, and a practical checklist you can apply to every job description you write. Everything here is grounded in published research and real-world hiring data.

The Business Case: Why This Matters Beyond Ethics

The data is unambiguous. Inclusive job descriptions produce larger, more diverse candidate pools without sacrificing quality. Companies that write inclusive postings fill roles faster because they are not artificially limiting their market. They make better hires because they are evaluating a broader range of backgrounds and perspectives. And diverse teams outperform homogeneous ones - not as a feel-good narrative, but as a measurable business outcome.

Gendered Language: The Invisible Filter

Research by Danielle Gaucher, Justin Friesen, and Aaron Kay at the University of Waterloo demonstrated that gendered wording in job advertisements has a measurable effect on who applies. Masculine-coded words signal that a role is male-dominated, discouraging women from applying even when they are fully qualified. The effect is not subtle - it is a primary driver of gender imbalance in applicant pools.

Masculine-coded words to avoid

Feminine-coded words to use carefully

While feminine-coded words (nurturing, supportive, collaborative, empathetic) do not deter male applicants as strongly as masculine-coded words deter female applicants, overuse can signal that a role is undervalued or lacks authority. Balance is the goal - describe the work accurately without leaning heavily on either gendered vocabulary set.

Requirement Inflation: The Qualification Trap

A Hewlett Packard internal report found that men apply to jobs when they meet about 60% of the qualifications, while women apply only when they meet 100%. This is not a confidence problem that women need to fix. It is a signal problem that employers create by listing aspirational wish lists as hard requirements.

When your job description lists 15 requirements and only 7 are actually necessary for the role, you have not set a high bar - you have set a misleading one. The candidates who apply despite not meeting all requirements tend to be men, not because men are bolder, but because research shows men interpret "requirements" as suggestions while women interpret them literally.

How to fix requirement lists

• Separate requirements from preferences. Label true must-haves as "Requirements" and nice-to-haves as "Preferred" or "Bonus." Be honest about which is which. If someone could learn a skill in the first 90 days on the job, it is not a requirement.
• Limit requirements to 5-7 items. If you cannot describe the essential qualifications in 5-7 bullet points, you do not understand the role well enough. Every item beyond that threshold reduces your applicant diversity without improving hire quality.
• Replace years-of-experience requirements with skill demonstrations. "8+ years of experience" tells you how long someone has been doing something, not how well they do it. "Demonstrated ability to architect distributed systems handling 10K+ requests per second" tells you what they can actually do. The first excludes a 25-year-old prodigy. The second does not.
• Remove degree requirements unless legally necessary. For the majority of roles, a specific degree is not predictive of performance. Requiring a bachelor's degree excludes roughly 60% of the US adult population - a disproportionate share of whom are Black, Hispanic, and from lower-income backgrounds. If the role requires specific knowledge, test for that knowledge, not the credential.
• Eliminate "culture fit" as a requirement. "Culture fit" is the most common proxy for "people who look, think, and act like us." Replace it with "culture add" - what perspectives and experiences does this person bring that we currently lack?

Before and after: Requirements section

The "before" version requires a specific degree, inflates experience requirements, demands mastery of four languages (when two are sufficient), uses masculine-coded language throughout, requires native English (illegal in many jurisdictions and irrelevant to the work), and adds geographic restrictions. The "after" version describes what the person actually needs to do the job. The applicant pool for the second version will be larger, more diverse, and just as qualified.

Readability and Accessibility

Inclusivity is not just about word choice - it is about whether people can actually read and understand your posting. Job descriptions riddled with jargon, acronyms, and complex sentence structures exclude non-native English speakers, neurodiverse candidates, and people from non-traditional backgrounds who may have the skills but not the vocabulary.

Readability guidelines

• Target an 8th to 10th grade reading level. Use the Flesch-Kincaid readability test to check. This is not about dumbing down the content - it is about clarity. Research shows that even highly educated readers prefer content written at lower reading levels because it is faster to process and less ambiguous.
• Define or avoid industry jargon. If you must use specialized terminology, explain what it means the first time you use it. Do not assume everyone knows what "CI/CD pipeline," "stakeholder alignment," or "cross-functional synergy" means.
• Use short paragraphs and bullet points. Dense blocks of text are difficult for everyone and particularly challenging for people with dyslexia, ADHD, or visual processing differences. Break information into scannable sections.
• Write in second person. "You will manage a team of five" is clearer and more engaging than "The ideal candidate will manage a team of five." Second person also avoids the awkward he/she/they pronoun question entirely.
• Avoid idioms and cultural references. "Hit the ground running," "move the needle," "drink the Kool-Aid" - these phrases are opaque to non-native speakers and carry cultural baggage that may be alienating. Say what you mean directly.

Accessibility requirements

• Alt text for images. If your job posting includes images, logos, or graphics, include descriptive alt text for screen readers.
• Sufficient color contrast. If your careers page uses custom styling, ensure text meets WCAG 2.1 AA contrast ratios (4.5:1 for normal text).
• Keyboard navigation. Ensure the application process can be completed without a mouse. Many candidates with motor disabilities use keyboard navigation or assistive devices.
• Include an accommodation statement. Add a clear statement like: "If you need accommodations during the application or interview process, please contact [email] and we will work with you to meet your needs." This is not just good practice - it is legally required in many jurisdictions under the ADA and equivalent legislation.

Structural Bias: Beyond Word Choice

Language is only one dimension of job description bias. The structure and content of the posting can also exclude candidates in ways that are not immediately obvious.

Salary transparency

Not posting salary ranges disproportionately harms women and people of color, who are statistically less likely to negotiate and more likely to accept offers below market rate. Salary transparency is now legally required in Colorado, New York City, California, Washington state, and several other jurisdictions - but even where it is not required, posting ranges is one of the highest-impact inclusion actions you can take.

Posting salary ranges also saves everyone time. A candidate who needs $150,000 will not waste their time - or yours - applying to a role that pays $90,000. Transparency is efficient.

Location and flexibility

Requiring on-site presence when the role can be done remotely excludes people with disabilities, caregivers (disproportionately women), people in geographic areas with limited opportunities, and anyone who cannot relocate. If the role can be done remotely, say so. If it requires on-site presence, explain why - candidates respect honesty about requirements far more than they respect requirements that seem arbitrary.

Application process

If your application requires a 45-minute form, a custom cover letter, three references, and a portfolio - before any human has looked at the candidate - you are selecting for people who have time and privilege, not people who are best qualified. Simplify. Name, email, resume, and one or two targeted questions are sufficient for initial screening. Save the detailed assessments for candidates who have been shortlisted.

Tools for Bias Detection

Even with the best intentions, unconscious bias is difficult to catch through manual review alone. These tools can help identify patterns you might miss.

Textio

The market leader in augmented writing for hiring. Textio analyzes your job description in real time and provides a score based on language inclusivity, tone, readability, and predicted appeal to different demographic groups. It suggests specific replacements for biased phrases and shows how similar postings performed in terms of applicant diversity. Textio's database includes outcomes from millions of job postings, so its recommendations are grounded in actual hiring data, not just linguistic theory.

Best for: Enterprise teams writing high volumes of job descriptions who need consistent quality and data-driven recommendations.

Gender Decoder

A free, open-source tool based on the Gaucher, Friesen, and Kay research. Paste your job description and it identifies masculine-coded and feminine-coded words. It does not provide readability analysis or general bias detection, but it is the fastest way to check for gendered language at zero cost.

Best for: Quick checks on individual job descriptions. Use it as a first pass before deeper analysis.

Ongig Text Analyzer

Goes beyond gendered language to check for age bias (words like "digital native," "young and energetic"), ability bias (phrases that assume physical capabilities), racial bias (culturally loaded terms), and readability. It also checks for exclusionary requirements and suggests more inclusive alternatives.

Best for: Companies that need comprehensive bias detection beyond just gender.

Applied

A recruitment platform that takes a structural approach to bias reduction. Rather than just analyzing language, Applied restructures the entire hiring process - anonymizing applications, randomizing candidate review order, and using structured scoring rubrics. Their job description builder includes bias checking, but the real value is the end-to-end process redesign.

Best for: Companies ready to overhaul their entire hiring process, not just their job descriptions.

Built-in ATS features

Most modern applicant tracking systems - Greenhouse, Lever, Workday, SmartRecruiters - now include basic bias-checking features in their job description editors. These are typically less sophisticated than dedicated tools, but they provide a baseline level of checking that catches the most obvious issues. If you already use an ATS, check whether it offers this feature before purchasing a separate tool.

Before and After: Complete Job Descriptions

Example: Marketing Manager

The "before" version uses seven masculine-coded words, assumes the candidate is male, requires a specific degree that is not predictive of performance, inflates experience requirements, illegally requires native English, and mandates five-day office presence. The "after" version describes the actual job, lists actual requirements, and provides the information candidates need to make an informed decision.

The Inclusive Job Description Checklist

Use this checklist every time you write or review a job description. It takes five minutes and will measurably improve your applicant diversity.

How WorkSwipe Supports Inclusive Hiring

Inclusive job descriptions get candidates to your door. What happens next determines whether inclusivity is real or performative. WorkSwipe's matching engine was designed to extend inclusive hiring principles through the entire process.

Skills-based matching eliminates proxy bias. Our algorithm evaluates what candidates can do, not where they went to school, what their name sounds like, or how many years appear on their resume. This means the non-traditional candidate with the right skills gets the same consideration as the Ivy League graduate. Learn more about our approach at How It Works.

Two-sided matching reduces structural power imbalances. Traditional hiring is one-sided - the employer evaluates, the candidate hopes. WorkSwipe's swipe-based model gives candidates equal agency in the matching process. Both sides must express interest. This structural design means that biased employers cannot simply filter out diverse candidates without consequence - candidates are also evaluating employers.

Writing an inclusive job description is the first step. Building an inclusive hiring process that delivers on that promise is the full journey. The tools, research, and checklist in this guide give you a concrete starting point. The candidates you have been missing are out there. Remove the barriers and let them find you.