How to Audit Your AI Resume Screening for Bias

Here's what keeps HR leaders up at night: Your AI resume screening tool is processing applications, making split-second decisions—but you have no idea if it's biased. And if it is, you could face lawsuits, fines, and bad press. The solution? Audit it. Now.

This isn't optional anymore. NYC Local Law 144 (effective July 2023) requires annual third-party bias audits before deploying AI hiring tools. Maryland, Illinois, and Colorado are following suit. Auditing isn't compliance theater—it's survival.

Q: Why can't we just trust the vendor that the AI is fair?

Because vendors have financial incentives to say "our AI is fair," not "our AI discriminates."

Research from 2025 shows: 98.4% of Fortune 500 companies use AI in hiring. Yet 67% acknowledge bias concerns. The gap? Most companies trust vendor promises instead of auditing themselves.

Real example: Workday's resume screening allegedly discriminated based on race, age 40+, and disability. The court (as of May 2025) allowed the Mobley v. Workday case to proceed as a collective action. This wasn't discovered by Workday's internal testing—it took lawsuits.

The point: Vendors won't flag their own bias. You have to.

Q: What exactly is the "impact ratio"? How do we measure it?

The impact ratio is how legal regulators measure fairness. Get it right and you're compliant. Get it wrong and you face fines.

Formula:
Impact Ratio = (Selection rate of underrepresented group) / (Selection rate of majority group)

Real example:
You feed your AI resume screening tool 1,000 female resumes and 1,000 male resumes (same skills, same experience, only name changed).

• Your AI selects 150 female resumes (15% selection rate)
• Your AI selects 200 male resumes (20% selection rate)
• Impact ratio = 15% / 20% = 0.75 (or 75%)

Is 75% acceptable? No. The EEOC four-fifths rule says: impact ratio must be 80% or higher. If you're below 80%, you have disparate impact—legal term for "your AI discriminates."

What does each impact ratio mean?

• 85-100%: Fair. Compliant. Green light.
• 80-84%: Borderline. Investigate why the gap exists.
• Below 80%: Disparate impact. Red flag. Fix immediately.

Real 2025 data: Unaudited AI resume screening tools show impact ratios of 60-75% for race and gender. Audited systems hit 85-95%. The difference? Deliberate testing and fixing.

Q: How do you actually test for name bias in your AI?

The methodology is simple but reveals shocking bias.

Step 1: Create identical resume pairs
Take one perfect resume. Duplicate it. Change only the name. Example:
Version A: "Emily Johnson" (majority name)
Version B: "Lakisha Brown" (Black-associated name)
Same skills, experience, achievements. Only the name differs.

Step 2: Feed both through your AI
Submit Emily's resume and Lakisha's resume to your AI resume screener. Don't tell it you're testing. Let it run normally.

Step 3: Compare scores
Did Emily score higher? By how much? This is name bias.

What do the numbers look like?
University of Washington 2024 research tested this extensively. Their findings:
- Racial bias was pronounced: resumes with white-associated names (85.1% selection) vs. Black-associated names (8.6% selection)
- Gender bias: male-associated names favored in 51.9% of cases vs. female names in 11.1%
- Intersectional bias: Black men's resumes selected 0% of the time compared to white men's names

Real talk: If your AI shows similar disparities, it has name bias. Fix it immediately or face liability.

Q: What about intersectional bias? That's different from gender OR race bias, right?

Yes. And most companies miss it because they test gender and race separately.

Here's the problem:
Audit approach 1 (wrong): "Test all female resumes vs. all male resumes." Result: "Gender impact ratio 82%. Compliant!"
Audit approach 2 (right): "Test Black women vs. white women vs. Black men vs. white men separately." Result: "Overall gender 82%, but Black women only 65%. Non-compliant."

The first approach misses the second. Intersectional bias is compound—some groups face multiple biases at once.

How to test intersectional bias:
Create test resumes across combinations:
- White women: Emily Johnson
- Black women: Lakisha Brown
- White men: John Smith
- Black men: Jamal Davis
- Latina women: Maria Garcia
- Asian women: Li Wong

Feed all through your AI. Calculate impact ratios for each group against majority (usually white men). If any group falls below 80%, you have intersectional bias.

Why it matters: Legal compliance requires testing intersectional categories (NYC Local Law 144 specifically mentions this). Also, finding and fixing intersectional bias means your diverse candidates get fair treatment.

Q: How do we test for age bias, disability bias, and other hidden discriminations?

These are proxy biases—indirect discrimination.

Age bias (illegal for 40+):
Test if your AI penalizes graduation dates from the 1980s-90s. Create two identical resumes:
- Resume A: "Graduated 2020, 3 years experience"
- Resume B: "Graduated 1995, 28 years experience"
Same skills listed. Only graduation date differs. Does the AI score one higher? That's proxy age discrimination.

Disability bias:
Does your AI penalize employment gaps (common for medical leave, cancer treatment, disability accommodation)? Test:
- Resume A: "2020-2025 continuous employment"
- Resume B: "2020-2022, gap 2022-2023, 2023-2025 employment"
Same skills. Does the gap hurt the score? If yes, you may be discriminating against candidates with disabilities.

Socioeconomic bias:
Does your AI favor Ivy League schools? Penalize bootcamps or community colleges? Test:
- Resume A: "Stanford CS degree"
- Resume B: "General Assembly bootcamp, same skills"
If one scores significantly higher for the same demonstrated skills, you have socioeconomic bias (which correlates with race/ethnicity).

Q: What tools should we use to run these audits?

You don't need expensive auditors. Free/low-cost tools exist.

1. IBM AI Fairness 360 (Open Source, Free)
What it does: Detects bias in your AI model. Supports 30+ fairness metrics. Generates reports on disparate impact ratio.
How to use: Feed your resume screener's data + decisions → IBM tool calculates impact ratios across demographics.
Best for: Technical teams comfortable with Python/code.

2. Google What-If Tool (Free)
What it does: Interactive visualization. Change one variable (name, experience, graduation date), watch how prediction changes.
Example: Swap "Emily" for "Lakisha," see if score drops.
Best for: Visual understanding of bias. Non-technical stakeholders can see the problem clearly.

3. External Third-Party Auditors
Firms like Humantic, FairNow, and others conduct professional bias audits (required by NYC law).
Cost: $5K-$50K depending on scope.
Benefit: Creates defensible documentation if sued. Shows good-faith compliance effort.

4. Your Vendor's Built-In Tools
Ask your resume screening vendor: "Do you provide fairness audit reports?" Good vendors (Pymetrics, Eightfold, Toggl Hire) publish impact ratios monthly. Bad vendors deflect or say "we don't track that."

Q: What's the step-by-step audit process?

Month 1: Define your fairness goal
What impact ratio threshold are you targeting? (80% minimum by EEOC, 85%+ best practice)
What demographic groups will you test? (gender, race, age, disability proxy, socioeconomic)
Document this. It shows intentionality if audited.

Month 2: Create test datasets
Build 100-500 identical resume pairs with only names/dates/schools changed.
Ensure diversity in the test names (not just "Emily vs. Lakisha," but include Asian names, Hispanic names, older-sounding names, etc.)

Month 3: Run audit
Feed test resumes through your resume screener. Collect scores.
Calculate impact ratios using: (minority selection rate) / (majority selection rate)
Document all findings.

Month 4: Analyze results
Is your impact ratio 80%+? If yes, document compliance. If no, investigate why:
- Are certain features (experience, school, graduation date) driving bias?
- Is it the training data (model learned discrimination)?
- Is it the algorithm design?

Month 5: Remediate
Options:
- Retrain the model on balanced data
- Adjust feature weights (reduce bias-prone features like school prestige)
- Use fairness constraints ("penalize selections that show gender bias")
- Switch tools (if vendor can't fix it)

Month 6: Re-test
Run audit again. Did impact ratio improve? Repeat until you hit 85%+ across all groups.

Q: How often should we audit?

NYC law requires annual audits. Best practice? Quarterly.

Why quarterly? Your AI sees new data every day. Bias can creep in (called "drift"). Quarterly audits catch this before it compounds.

Audit calendar:
Q1 (Jan-Mar): Run full audit. Test all demographics.
Q2 (Apr-Jun): Spot check. Test name bias only.
Q3 (Jul-Sep): Full audit. Everything again.
Q4 (Oct-Dec): Spot check. Name bias + age proxy bias.

If you find bias in any quarter, audit monthly until fixed.

Q: What are red flags that mean your AI is discriminating?

Impact ratio below 80%: Obvious red flag. Legal disparate impact.

Impact ratio 80-85%: Borderline. Investigate. If trend continues, it's drifting worse.

Unexplained variance: One demographic group's impact ratio 92%, another 71%. Why? Find the cause. Could be bad training data, biased features, or algorithm design.

Vendor refuses to audit: Biggest red flag. Good vendors WANT to prove fairness. Bad vendors hide numbers. Walk away.

Your own bias: You notice your AI consistently rejects resumes from certain schools, geographies, or name patterns? That's discrimination waiting to be discovered by a lawsuit.

Q: When you find bias, what do you actually do?

Three-step fix process:

Step 1: Document everything
"On November 13, 2025, we audited our resume screener. Found 72% impact ratio for Black candidates. Below 80% threshold. Constitutes potential disparate impact."
Documentation = evidence of good faith. If sued, this shows you tried to be fair.

Step 2: Pause deployment (if new tool)
Don't deploy a biased system. If already deployed, continue using it but flag decisions for human review. "AI recommended rejection, but impact ratio suggests name bias. Escalate to human recruiter."

Step 3: Remediate
Work with vendor to fix it. Retrain on balanced data. Adjust feature weights. Add fairness constraints. Once you hit 85%+, document that too.

Q: What questions should we ask our resume screening vendor?

Before buying, ask:

1. "Can you provide your latest bias audit report? What was your impact ratio by gender, race, and age?"
Don't accept vague answers. Demand numbers.

2. "How often do you audit for bias?"
Annually (minimum, NYC requirement). Quarterly (best practice).

3. "What happens if we find bias during our own audit?"
Will they retrain the model at no cost? Or will they blame you for bad data?

4. "Is your AI explainable? Can you tell me why it scored a candidate 7.2/10?"
Black-box AI is liability. Demand explainability.

5. "Do you offer fairness constraints? Can we set minimum diversity targets?"
Good vendors let you program fairness. Bad ones say "that's not possible."

The Real Talk

• Auditing is not optional. NYC, Maryland, Illinois, Colorado require it. More states coming.
• 80% impact ratio is the legal minimum (EEOC four-fifths rule). 85%+ is compliant best practice.
• Name bias is real and measurable. University of Washington found massive disparities. Yours likely has it too.
• Intersectional testing catches bias that gender-only or race-only testing misses.
• Quarterly audits beat annual audits. Bias drifts. Catch it early.
• Vendor promises mean nothing. Test it yourself.
• If you find bias, document everything. Documentation is compliance evidence.
• Fair AI hires better candidates. It's not about being nice—it's about finding talent your biased screening was missing.

Ready to audit your resume screening?

HR AGENT LABS includes built-in fairness auditing, impact ratio tracking, and compliance reporting. Test for bias quarterly. Know your impact ratios. Stay compliant with NYC, Maryland, and upcoming state laws. We help you prove your AI resume screening is fair—with documentation, metrics, and transparency. No guessing. No lawsuits.

Related reads:

• Best Practices for Bias-Free AI Resume Screening
• Is AI Resume Screening Really Bias-Free? 2025 Research
• How Companies Use AI to Improve Diversity Hiring

Discuss this:

• Join our forum – Share your audit findings and bias remediation strategies
• r/aiethics – AI bias detection, fairness testing, and responsible AI discussions
• AI in Recruitment Group – Audit methodologies, compliance, and best practices