How to Use AI to Write Unbiased Job Descriptions (2025 Guide)

You might be rejecting the perfect candidate before they even click "Apply." Language is subtle, powerful, and often unintentionally exclusionary.

If you are writing a standard job post for a "Software Engineer" but use words like "Ninja," "Rockstar," or "Dominate," research shows you are statistically reducing the number of female applicants by up to 40%. This isn't just a matter of political correctness; it is a matter of business math. By shrinking your funnel at the top, you increase your Cost of a Bad Hire and extend your time-to-fill.

Unconscious bias in job descriptions (JDs) is one of the biggest bottlenecks in modern recruitment. It extends beyond gender to include ageism ("Digital Native"), ableism ("Must lift 20lbs"), and educational elitism.

The good news? Artificial Intelligence is the perfect unbiased editor. Unlike a human hiring manager, who carries a lifetime of subconscious preferences, AI can be trained to spot exclusionary patterns instantly. In this guide, we will show you how to use tools like ChatGPT, Claude, and Textio to neutralize your language and widen your talent pool immediately.

The Hidden Cost of "Bro-Culture" Copy

Why does this matter? Is this just HR fluff? No, it is about ROI (Return on Investment).

When your job description contains biased language, you are artificially shrinking your talent pool. If you use "masculine-coded" language, qualified female candidates often opt out, assuming the culture won't be a good fit. If you use ageist language, experienced veterans with 20 years of institutional knowledge won't apply.

The Data: A seminal study published in the Journal of Personality and Social Psychology found that masculine-coded words (e.g., competitive, dominant, leader) deter women from applying to jobs, even if they are 100% qualified. Conversely, feminine-coded words (e.g., support, understand, interpersonal) do not deter men. This creates a market inefficiency that you can exploit: neutralize your ads, and you get access to the talent your competitors are scaring away.

Decoding the 3 Types of Bias

Before we use AI to fix it, we need to understand what we are looking for. Most biased JDs fall into three major traps:

1. Gender Bias (The "Rockstar" Problem)

This is the most common and widely researched form of bias. It involves using sports metaphors, war terminology, or aggressive adjectives that signal a hyper-competitive, aggressive workplace.

2. Ageism (The "Digital Native" Trap)

This is rampant in the tech and marketing sectors. Terms that imply you are looking for someone young are not only exclusionary but often illegal in the US and UK under employment discrimination laws.

3. Ableism (The "Must Lift 50lbs" Default)

This often appears in the "Requirements" section due to copy-pasting old templates. Do you really need your Accountant to "lift 50 lbs"? Probably not. Including physical requirements for desk jobs discriminates against people with disabilities.

Similarly, vague requirements like "Must have strong communication skills" can sometimes bias against neurodivergent candidates (like those with Autism) who might be incredible coders but communicate differently. Be specific: "Must be able to document code clearly" is a better requirement.

Step-by-Step: Using ChatGPT to Audit Your JDs

You don't need expensive enterprise software to start fixing this today. If you have ChatGPT (Free or Plus), Claude, or Gemini, you can use these specific prompts to audit your current listings.

Prompt 1: The "DEI Audit"

Use this prompt to identify the problems. Do not ask it to rewrite yet; ask it to explain the bias first so you can learn.

Prompt 2: The "Skills-First" Rewrite

Often, we ask for degrees or years of experience that aren't actually necessary. This is called "Credentialism," and it filters out talented self-taught developers or people from non-traditional backgrounds.

Prompt 3: The Readability Fix

If your job description is a wall of text with complex sentences, you discourage people who speak English as a second language (ESL) or neurodivergent candidates.

The "Confidence Gap": Why Must-Haves Matter

There is a famous statistic from an internal Hewlett-Packard report: Men apply for a job when they meet only 60% of the qualifications, but women apply only if they meet 100% of them.

To combat this "Confidence Gap," you must rigorously edit your bullet points.

• Separate Essentials: Clearly distinguish between "Required Skills" and "Nice-to-Have Skills."
• Limit Requirements: Try to keep the mandatory list to 5-7 bullet points maximum. If you list 20 requirements, you are unintentionally filtering out women and honest candidates who don't want to lie.
• Add a Disclaimer: We recommend adding this sentence to the bottom of every JD: "Studies show that women and underrepresented groups are less likely to apply if they don't meet 100% of the criteria. If you think you have what it takes, we encourage you to apply."

Top Dedicated Tools (Beyond ChatGPT)

While ChatGPT is a great generalist tool, specialized software offers deeper analytics and integration with your Applicant Tracking System (ATS).

1. Textio
The industry leader. Textio uses a massive dataset of hiring outcomes to predict how your language will perform. It gives your JD a score (0-100) and highlights phrases that will speed up or slow down hiring. It is expensive, but for enterprise teams, it is the gold standard.

2. Gender Decoder (Free)
A simple, web-based tool created by Kat Matfield. You paste your text, and it highlights masculine vs. feminine coded words based on the academic paper mentioned earlier. It doesn't offer rewrites, but it's a great quick check.

3. Datapeople
Datapeople focuses on the entire funnel. It tells you if your "Requirements" list is too long (which deters women) or if your title is confusing. It helps standardize job titles across large organizations to ensure pay equity.

Best Practices: Don't Let AI Remove the "Soul"

There is a risk when using AI: you might sanitize your job description so much that it becomes boring. A job ad is still an ad. You need to sell the role.

The Balance:

• Do remove exclusionary language (Ninja, Rockstar, Digital Native).
• Do keep your company's unique voice. If you are a fun, casual startup, you can still sound fun without being biased.
• Don't lie about the culture. If your company is highly competitive and aggressive, using neutral language might attract candidates who will hate working there. Be honest, but professional.

Legal Considerations in 2025

As AI becomes more integrated into hiring, governments are catching up. New York City’s Local Law 144 now requires bias audits for automated employment decision tools. While this mostly applies to AI Resume Screening Tools, the principle is spreading to all parts of the hiring funnel.

Using AI to write descriptions is generally safe, but using AI to filter candidates based on those descriptions is where liability starts. Always ensure a human is the final decision maker. Read more in our guide to AI Recruitment Laws.

Frequently Asked Questions