AI won't fix hiring bias — unless you fix the data first
How Vire is tackling bias head-on by rethinking what data we collect (and what we don't).
June 13, 2025
The recent Workday lawsuit has once again put a spotlight on one of AI's ugliest vulnerabilities: when trained on biased data, even the most sophisticated algorithms can amplify existing discrimination — not eliminate it.
In the Workday case, the plaintiff alleges that the platform's automated screening tools led to unfair treatment of candidates based on race, age, and disability. Whether or not the case succeeds in court, one thing is clear for every hiring leader watching:
👉 Speed isn't worth it if fairness gets sacrificed.
The real problem isn't the algorithm — it's what you feed it.
Most AI hiring tools are built on top of resume data.
That's already a problem. Resumes reflect unequal systems:
- Educational access gaps.
- Career breaks (often penalizing parents and caregivers).
- Company name signaling (favoring prestige over capability).
- Keyword gaming (rewarding those who know how to write for ATS filters).
If your AI is only scanning prior job titles, university degrees, and bullet-point buzzwords — you're simply automating the same biased heuristics humans have always used.
Garbage in → garbage out.
Or worse: Bias in → bias amplified.
At Vire, we chose a harder path: change the input.
We don't think you can solve bias just by adding better filters to bad data.
You need to collect different data.
That's why Vire starts by explicitly asking candidates questions that matter — and don't rely on proxies that correlate with bias:
- Motivation: What drives this person? Where do they want to grow?
- Work style: What environment helps them thrive?
- Problem-solving depth: What types of challenges have they navigated?
- Values alignment: What missions feel meaningful to them?
- Transferable expertise: What skills and experiences translate across roles, regardless of job title?
Instead of assuming that two people with the same "Senior Product Manager" title bring identical strengths — we let candidates articulate what they've actually built, how they've led, and where they shine.
More signal. Less noise. Fewer shortcuts.
By starting with deeper, structured inputs:
✅ We give every candidate a chance to present their full story — not just their resume formatting skills.
✅ We reduce reliance on heuristics like pedigree, gaps, or job-hopping.
✅ We give employers far richer signal to evaluate actual fit — mission alignment, team compatibility, growth trajectory.
The result? Less room for unconscious bias to creep in. And a decision-making process that's supported by data you can actually stand behind.
The right kind of AI doesn't replace judgment. It makes judgment better.
Vire isn't an auto-rejection engine.
We don't score candidates behind a black box.
We don't encourage you to "trust the model."
Instead, we give your recruiters and hiring managers higher-signal profiles that empower better, more human decisions:
- Structured data → for clear, consistent comparisons.
- Mission + environment fit → to reduce downstream misalignment.
- Context-rich profiles → to minimize snap judgments based on surface proxies.
If we want AI to reduce bias, we have to design for it — from the ground up.
AI isn't inherently biased or unbiased.
But it is a mirror. And right now, too many systems are simply reflecting back the inequalities baked into traditional hiring pipelines.
At Vire, we believe the only real fix is to reimagine what data matters.
Not what you've listed. ⏩ But what you bring.
Not just where you've been. ⏩ But where you're capable of going.
Because fair hiring doesn't start at the rejection filter.
It starts at who gets to be fully seen in the first place.
✨ Learn how Vire is helping companies make better, fairer hiring decisions.